Project: Identify Customer Segments¶

In this project, you will apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that you will use has been provided by our partners at Bertelsmann Arvato Analytics, and represents a real-life data science task.

This notebook will help you complete this task by providing a framework within which you will perform your analysis steps. In each step of the project, you will see some text describing the subtask that you will perform, followed by one or more code cells for you to complete your work. Feel free to add additional code and markdown cells as you go along so that you can explore everything in precise chunks. The code cells provided in the base template will outline only the major tasks, and will usually not be enough to cover all of the minor tasks that comprise it.

It should be noted that while there will be precise guidelines on how you should handle certain tasks in the project, there will also be places where an exact specification is not provided. There will be times in the project where you will need to make and justify your own decisions on how to treat the data. These are places where there may not be only one way to handle the data. In real-life tasks, there may be many valid ways to approach an analysis task. One of the most important things you can do is clearly document your approach so that other scientists can understand the decisions you've made.

At the end of most sections, there will be a Markdown cell labeled Discussion. In these cells, you will report your findings for the completed section, as well as document the decisions that you made in your approach to each subtask. Your project will be evaluated not just on the code used to complete the tasks outlined, but also your communication about your observations and conclusions at each stage.

In [1]:
# import libraries here; add more as necessary
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

import sklearn
from sklearn.preprocessing import OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
# magic word for producing visualizations in notebook
%matplotlib inline

pd.options.display.max_rows = None
pd.options.display.max_columns = None

Step 0: Load the Data¶

There are four files associated with this project (not including this one):

  • Udacity_AZDIAS_Subset.csv: Demographics data for the general population of Germany; 891211 persons (rows) x 85 features (columns).
  • Udacity_CUSTOMERS_Subset.csv: Demographics data for customers of a mail-order company; 191652 persons (rows) x 85 features (columns).
  • Data_Dictionary.md: Detailed information file about the features in the provided datasets.
  • AZDIAS_Feature_Summary.csv: Summary of feature attributes for demographics data; 85 features (rows) x 4 columns

Each row of the demographics files represents a single person, but also includes information outside of individuals, including information about their household, building, and neighborhood. You will use this information to cluster the general population into groups with similar demographic properties. Then, you will see how the people in the customers dataset fit into those created clusters. The hope here is that certain clusters are over-represented in the customers data, as compared to the general population; those over-represented clusters will be assumed to be part of the core userbase. This information can then be used for further applications, such as targeting for a marketing campaign.

To start off with, load in the demographics data for the general population into a pandas DataFrame, and do the same for the feature attributes summary. Note for all of the .csv data files in this project: they're semicolon (;) delimited, so you'll need an additional argument in your read_csv() call to read in the data properly. Also, considering the size of the main dataset, it may take some time for it to load completely.

Once the dataset is loaded, it's recommended that you take a little bit of time just browsing the general structure of the dataset and feature summary file. You'll be getting deep into the innards of the cleaning in the first major step of the project, so gaining some general familiarity can help you get your bearings.

In [2]:
# Load in the general demographics data.
azdias_demographics_data = pd.read_csv("Udacity_AZDIAS_Subset.csv", sep=';')
# Load in the feature summary file.
azdias_feature_summary = pd.read_csv("AZDIAS_Feature_Summary.csv", sep=';')
In [3]:
# Check the structure of the data after it's loaded (e.g. print the number of
# rows and columns, print the first few rows).
n_rows_azdias_demographics_data, n_cols_azdias_demographics_data = azdias_demographics_data.shape
n_rows_azdias_feature_summary, n_cols_azdias_feature_summary = azdias_feature_summary.shape
print('='*50)
print(f'azdias demographics data has {n_rows_azdias_demographics_data} rows')
print(f'azdias demographics ata has {n_cols_azdias_demographics_data} columns')
print('='*50)
print(f'azdias feature summary has {n_rows_azdias_feature_summary} rows')
print(f'azdias feature summary {n_cols_azdias_feature_summary} columns')

==================================================
azdias demographics data has 891221 rows
azdias demographics ata has 85 columns
==================================================
azdias feature summary has 85 rows
azdias feature summary 4 columns

Tip: Add additional cells to keep everything in reasonably-sized chunks! Keyboard shortcut esc --> a (press escape to enter command mode, then press the 'A' key) adds a new cell before the active cell, and esc --> b adds a new cell after the active cell. If you need to convert an active cell to a markdown cell, use esc --> m and to convert to a code cell, use esc --> y.

Step 1: Preprocessing¶

Step 1.1: Assess Missing Data¶

The feature summary file contains a summary of properties for each demographics data column. You will use this file to help you make cleaning decisions during this stage of the project. First of all, you should assess the demographics data in terms of missing data. Pay attention to the following points as you perform your analysis, and take notes on what you observe. Make sure that you fill in the Discussion cell with your findings and decisions at the end of each step that has one!

Step 1.1.1: Convert Missing Value Codes to NaNs¶

The fourth column of the feature attributes summary (loaded in above as feat_info) documents the codes from the data dictionary that indicate missing or unknown data. While the file encodes this as a list (e.g. [-1,0]), this will get read in as a string object. You'll need to do a little bit of parsing to make use of it to identify and clean the data. Convert data that matches a 'missing' or 'unknown' value code into a numpy NaN value. You might want to see how much data takes on a 'missing' or 'unknown' code, and how much data is naturally missing, as a point of interest.

As one more reminder, you are encouraged to add additional cells to break up your analysis into manageable chunks.

In [4]:
azdias_demographics_data.head()
Out[4]:
AGER_TYP ALTERSKATEGORIE_GROB ANREDE_KZ CJT_GESAMTTYP FINANZ_MINIMALIST FINANZ_SPARER FINANZ_VORSORGER FINANZ_ANLEGER FINANZ_UNAUFFAELLIGER FINANZ_HAUSBAUER FINANZTYP GEBURTSJAHR GFK_URLAUBERTYP GREEN_AVANTGARDE HEALTH_TYP LP_LEBENSPHASE_FEIN LP_LEBENSPHASE_GROB LP_FAMILIE_FEIN LP_FAMILIE_GROB LP_STATUS_FEIN LP_STATUS_GROB NATIONALITAET_KZ PRAEGENDE_JUGENDJAHRE RETOURTYP_BK_S SEMIO_SOZ SEMIO_FAM SEMIO_REL SEMIO_MAT SEMIO_VERT SEMIO_LUST SEMIO_ERL SEMIO_KULT SEMIO_RAT SEMIO_KRIT SEMIO_DOM SEMIO_KAEM SEMIO_PFLICHT SEMIO_TRADV SHOPPER_TYP SOHO_KZ TITEL_KZ VERS_TYP ZABEOTYP ALTER_HH ANZ_PERSONEN ANZ_TITEL HH_EINKOMMEN_SCORE KK_KUNDENTYP W_KEIT_KIND_HH WOHNDAUER_2008 ANZ_HAUSHALTE_AKTIV ANZ_HH_TITEL GEBAEUDETYP KONSUMNAEHE MIN_GEBAEUDEJAHR OST_WEST_KZ WOHNLAGE CAMEO_DEUG_2015 CAMEO_DEU_2015 CAMEO_INTL_2015 KBA05_ANTG1 KBA05_ANTG2 KBA05_ANTG3 KBA05_ANTG4 KBA05_BAUMAX KBA05_GBZ BALLRAUM EWDICHTE INNENSTADT GEBAEUDETYP_RASTER KKK MOBI_REGIO ONLINE_AFFINITAET REGIOTYP KBA13_ANZAHL_PKW PLZ8_ANTG1 PLZ8_ANTG2 PLZ8_ANTG3 PLZ8_ANTG4 PLZ8_BAUMAX PLZ8_HHZ PLZ8_GBZ ARBEIT ORTSGR_KLS9 RELAT_AB
0 -1 2 1 2.0 3 4 3 5 5 3 4 0 10.0 0 -1 15.0 4.0 2.0 2.0 1.0 1.0 0 0 5.0 2 6 7 5 1 5 3 3 4 7 6 6 5 3 -1 NaN NaN -1 3 NaN NaN NaN 2.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 -1 1 2 5.0 1 5 2 5 4 5 1 1996 10.0 0 3 21.0 6.0 5.0 3.0 2.0 1.0 1 14 1.0 5 4 4 3 1 2 2 3 6 4 7 4 7 6 3 1.0 0.0 2 5 0.0 2.0 0.0 6.0 NaN 3.0 9.0 11.0 0.0 8.0 1.0 1992.0 W 4.0 8 8A 51 0.0 0.0 0.0 2.0 5.0 1.0 6.0 3.0 8.0 3.0 2.0 1.0 3.0 3.0 963.0 2.0 3.0 2.0 1.0 1.0 5.0 4.0 3.0 5.0 4.0
2 -1 3 2 3.0 1 4 1 2 3 5 1 1979 10.0 1 3 3.0 1.0 1.0 1.0 3.0 2.0 1 15 3.0 4 1 3 3 4 4 6 3 4 7 7 7 3 3 2 0.0 0.0 1 5 17.0 1.0 0.0 4.0 NaN 3.0 9.0 10.0 0.0 1.0 5.0 1992.0 W 2.0 4 4C 24 1.0 3.0 1.0 0.0 0.0 3.0 2.0 4.0 4.0 4.0 2.0 3.0 2.0 2.0 712.0 3.0 3.0 1.0 0.0 1.0 4.0 4.0 3.0 5.0 2.0
3 2 4 2 2.0 4 2 5 2 1 2 6 1957 1.0 0 2 0.0 0.0 0.0 0.0 9.0 4.0 1 8 2.0 5 1 2 1 4 4 7 4 3 4 4 5 4 4 1 0.0 0.0 1 3 13.0 0.0 0.0 1.0 NaN NaN 9.0 1.0 0.0 1.0 4.0 1997.0 W 7.0 2 2A 12 4.0 1.0 0.0 0.0 1.0 4.0 4.0 2.0 6.0 4.0 0.0 4.0 1.0 0.0 596.0 2.0 2.0 2.0 0.0 1.0 3.0 4.0 2.0 3.0 3.0
4 -1 3 1 5.0 4 3 4 1 3 2 5 1963 5.0 0 3 32.0 10.0 10.0 5.0 3.0 2.0 1 8 5.0 6 4 4 2 7 4 4 6 2 3 2 2 4 2 2 0.0 0.0 2 4 20.0 4.0 0.0 5.0 1.0 2.0 9.0 3.0 0.0 1.0 4.0 1992.0 W 3.0 6 6B 43 1.0 4.0 1.0 0.0 0.0 3.0 2.0 5.0 1.0 5.0 3.0 3.0 5.0 5.0 435.0 2.0 4.0 2.0 1.0 2.0 3.0 3.0 4.0 6.0 5.0
In [5]:
azdias_demographics_data.describe()
Out[5]:
AGER_TYP ALTERSKATEGORIE_GROB ANREDE_KZ CJT_GESAMTTYP FINANZ_MINIMALIST FINANZ_SPARER FINANZ_VORSORGER FINANZ_ANLEGER FINANZ_UNAUFFAELLIGER FINANZ_HAUSBAUER FINANZTYP GEBURTSJAHR GFK_URLAUBERTYP GREEN_AVANTGARDE HEALTH_TYP LP_LEBENSPHASE_FEIN LP_LEBENSPHASE_GROB LP_FAMILIE_FEIN LP_FAMILIE_GROB LP_STATUS_FEIN LP_STATUS_GROB NATIONALITAET_KZ PRAEGENDE_JUGENDJAHRE RETOURTYP_BK_S SEMIO_SOZ SEMIO_FAM SEMIO_REL SEMIO_MAT SEMIO_VERT SEMIO_LUST SEMIO_ERL SEMIO_KULT SEMIO_RAT SEMIO_KRIT SEMIO_DOM SEMIO_KAEM SEMIO_PFLICHT SEMIO_TRADV SHOPPER_TYP SOHO_KZ TITEL_KZ VERS_TYP ZABEOTYP ALTER_HH ANZ_PERSONEN ANZ_TITEL HH_EINKOMMEN_SCORE KK_KUNDENTYP W_KEIT_KIND_HH WOHNDAUER_2008 ANZ_HAUSHALTE_AKTIV ANZ_HH_TITEL GEBAEUDETYP KONSUMNAEHE MIN_GEBAEUDEJAHR WOHNLAGE KBA05_ANTG1 KBA05_ANTG2 KBA05_ANTG3 KBA05_ANTG4 KBA05_BAUMAX KBA05_GBZ BALLRAUM EWDICHTE INNENSTADT GEBAEUDETYP_RASTER KKK MOBI_REGIO ONLINE_AFFINITAET REGIOTYP KBA13_ANZAHL_PKW PLZ8_ANTG1 PLZ8_ANTG2 PLZ8_ANTG3 PLZ8_ANTG4 PLZ8_BAUMAX PLZ8_HHZ PLZ8_GBZ ARBEIT ORTSGR_KLS9 RELAT_AB
count 891221.000000 891221.000000 891221.000000 886367.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 886367.000000 891221.000000 891221.000000 886367.000000 886367.000000 886367.000000 886367.000000 886367.000000 886367.000000 891221.000000 891221.000000 886367.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 891221.000000 817722.000000 817722.000000 891221.000000 891221.000000 817722.000000 817722.000000 817722.000000 872873.000000 306609.000000 783619.000000 817722.000000 798073.000000 794213.000000 798073.000000 817252.000000 798073.000000 798073.000000 757897.000000 757897.000000 757897.000000 757897.000000 757897.000000 757897.000000 797481.000000 797481.000000 797481.000000 798066.000000 770025.000000 757897.000000 886367.000000 770025.000000 785421.000000 774706.000000 774706.000000 774706.000000 774706.000000 774706.000000 774706.000000 774706.000000 794005.000000 794005.000000 794005.00000
mean -0.358435 2.777398 1.522098 3.632838 3.074528 2.821039 3.401106 3.033328 2.874167 3.075121 3.790586 1101.178533 7.350304 0.196612 1.792102 14.622637 4.453621 3.599574 2.185966 4.791151 2.432575 1.026827 8.154346 3.419630 3.945860 4.272729 4.240609 4.001597 4.023709 4.359086 4.481405 4.025014 3.910139 4.763223 4.667550 4.445007 4.256076 3.661784 1.266967 0.008423 0.003483 1.197852 3.362438 10.864126 1.727637 0.004162 4.207243 3.410640 3.933406 7.908791 8.287263 0.040647 2.798641 3.018452 1993.277011 4.052836 1.494277 1.265584 0.624525 0.305927 1.389552 3.158580 4.153043 3.939172 4.549491 3.738306 2.592991 2.963540 2.698691 4.257967 619.701439 2.253330 2.801858 1.595426 0.699166 1.943913 3.612821 3.381087 3.167854 5.293002 3.07222
std 1.198724 1.068775 0.499512 1.595021 1.321055 1.464749 1.322134 1.529603 1.486731 1.353248 1.987876 976.583551 3.525723 0.397437 1.269062 12.616883 3.855639 3.926486 1.756537 3.425305 1.474315 0.586634 4.844532 1.417741 1.946564 1.915885 2.007373 1.857540 2.077746 2.022829 1.807552 1.903816 1.580306 1.830789 1.795712 1.852412 1.770137 1.707637 1.287435 0.091392 0.084957 0.952532 1.352704 7.639683 1.155849 0.068855 1.624057 1.628844 1.964701 1.923137 15.628087 0.324028 2.656713 1.550312 3.332739 1.949539 1.403961 1.245178 1.013443 0.638725 1.779483 1.329537 2.183710 1.718996 2.028919 0.923193 1.119052 1.428882 1.521524 2.030385 340.034318 0.972008 0.920309 0.986736 0.727137 1.459654 0.973967 1.111598 1.002376 2.303739 1.36298
min -1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.000000 1.000000 0.000000 -1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 0.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 -1.000000 0.000000 0.000000 -1.000000 1.000000 0.000000 0.000000 0.000000 1.000000 1.000000 0.000000 1.000000 0.000000 0.000000 1.000000 1.000000 1985.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 1.000000 1.000000 0.000000 1.00000
25% -1.000000 2.000000 1.000000 2.000000 2.000000 1.000000 3.000000 2.000000 2.000000 2.000000 2.000000 0.000000 5.000000 0.000000 1.000000 4.000000 1.000000 1.000000 1.000000 2.000000 1.000000 1.000000 5.000000 2.000000 2.000000 3.000000 3.000000 2.000000 2.000000 2.000000 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000 2.000000 0.000000 0.000000 0.000000 1.000000 3.000000 0.000000 1.000000 0.000000 3.000000 2.000000 2.000000 8.000000 1.000000 0.000000 1.000000 2.000000 1992.000000 3.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2.000000 2.000000 2.000000 3.000000 3.000000 2.000000 2.000000 1.000000 3.000000 384.000000 1.000000 2.000000 1.000000 0.000000 1.000000 3.000000 3.000000 3.000000 4.000000 2.00000
50% -1.000000 3.000000 2.000000 4.000000 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000 4.000000 1943.000000 8.000000 0.000000 2.000000 11.000000 3.000000 1.000000 1.000000 4.000000 2.000000 1.000000 8.000000 3.000000 4.000000 4.000000 4.000000 4.000000 4.000000 5.000000 4.000000 4.000000 4.000000 5.000000 5.000000 5.000000 4.000000 3.000000 1.000000 0.000000 0.000000 1.000000 3.000000 13.000000 1.000000 0.000000 5.000000 3.000000 4.000000 9.000000 4.000000 0.000000 1.000000 3.000000 1992.000000 3.000000 1.000000 1.000000 0.000000 0.000000 1.000000 3.000000 5.000000 4.000000 5.000000 4.000000 3.000000 3.000000 3.000000 5.000000 549.000000 2.000000 3.000000 2.000000 1.000000 1.000000 4.000000 3.000000 3.000000 5.000000 3.00000
75% -1.000000 4.000000 2.000000 5.000000 4.000000 4.000000 5.000000 5.000000 4.000000 4.000000 6.000000 1970.000000 10.000000 0.000000 3.000000 27.000000 8.000000 8.000000 4.000000 9.000000 4.000000 1.000000 14.000000 5.000000 6.000000 6.000000 6.000000 5.000000 6.000000 6.000000 6.000000 5.000000 5.000000 6.000000 6.000000 6.000000 6.000000 5.000000 2.000000 0.000000 0.000000 2.000000 4.000000 17.000000 2.000000 0.000000 6.000000 5.000000 6.000000 9.000000 9.000000 0.000000 3.000000 4.000000 1993.000000 5.000000 3.000000 2.000000 1.000000 0.000000 3.000000 4.000000 6.000000 6.000000 6.000000 4.000000 3.000000 4.000000 4.000000 6.000000 778.000000 3.000000 3.000000 2.000000 1.000000 3.000000 4.000000 4.000000 4.000000 7.000000 4.00000
max 3.000000 9.000000 2.000000 6.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 6.000000 2017.000000 12.000000 1.000000 3.000000 40.000000 12.000000 11.000000 5.000000 10.000000 5.000000 3.000000 15.000000 5.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 3.000000 1.000000 5.000000 2.000000 6.000000 21.000000 45.000000 6.000000 6.000000 6.000000 6.000000 9.000000 595.000000 23.000000 8.000000 7.000000 2016.000000 8.000000 4.000000 4.000000 3.000000 2.000000 5.000000 5.000000 7.000000 6.000000 8.000000 5.000000 4.000000 6.000000 5.000000 7.000000 2300.000000 4.000000 4.000000 3.000000 2.000000 5.000000 5.000000 5.000000 9.000000 9.000000 9.00000
In [6]:
azdias_feature_summary.head()
Out[6]:
attribute information_level type missing_or_unknown
0 AGER_TYP person categorical [-1,0]
1 ALTERSKATEGORIE_GROB person ordinal [-1,0,9]
2 ANREDE_KZ person categorical [-1,0]
3 CJT_GESAMTTYP person categorical [0]
4 FINANZ_MINIMALIST person ordinal [-1]
In [7]:
azdias_feature_summary.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 85 entries, 0 to 84
Data columns (total 4 columns):
 #   Column              Non-Null Count  Dtype 
---  ------              --------------  ----- 
 0   attribute           85 non-null     object
 1   information_level   85 non-null     object
 2   type                85 non-null     object
 3   missing_or_unknown  85 non-null     object
dtypes: object(4)
memory usage: 2.8+ KB
In [8]:
def convert_str_to_list(string_list):
    result_lits = []
    string_list = string_list[1:-1] #remove the opening and closing brackets [ ]
    string_values = string_list.split(',')
    for val in string_values:
        try:
            result_lits.append(int(val))
        except:
            result_lits.append(val)
    return result_lits

missing_values = azdias_feature_summary['missing_or_unknown'].apply(convert_str_to_list)
In [9]:
for attribute, missing_vals in zip(azdias_feature_summary['attribute'], missing_values):
    if len(missing_vals)>0:
        for missing_value in missing_vals:
            azdias_demographics_data.loc[azdias_demographics_data[attribute] == missing_value, attribute] = np.nan

Step 1.1.2: Assess Missing Data in Each Column¶

How much missing data is present in each column? There are a few columns that are outliers in terms of the proportion of values that are missing. You will want to use matplotlib's hist() function to visualize the distribution of missing value counts to find these columns. Identify and document these columns. While some of these columns might have justifications for keeping or re-encoding the data, for this project you should just remove them from the dataframe. (Feel free to make remarks about these outlier columns in the discussion, however!)

For the remaining features, are there any patterns in which columns have, or share, missing data?

In [10]:
nans_values = (azdias_demographics_data.isna().sum()/azdias_demographics_data.shape[0]*100).sort_values(ascending=True)
nans_values
Out[10]:
ZABEOTYP                  0.000000
SEMIO_TRADV               0.000000
SEMIO_PFLICHT             0.000000
SEMIO_KAEM                0.000000
SEMIO_DOM                 0.000000
SEMIO_KRIT                0.000000
SEMIO_RAT                 0.000000
SEMIO_KULT                0.000000
SEMIO_ERL                 0.000000
SEMIO_LUST                0.000000
SEMIO_VERT                0.000000
SEMIO_MAT                 0.000000
SEMIO_REL                 0.000000
SEMIO_SOZ                 0.000000
SEMIO_FAM                 0.000000
FINANZ_VORSORGER          0.000000
ANREDE_KZ                 0.000000
FINANZ_MINIMALIST         0.000000
FINANZ_SPARER             0.000000
FINANZ_ANLEGER            0.000000
FINANZ_UNAUFFAELLIGER     0.000000
FINANZ_HAUSBAUER          0.000000
GREEN_AVANTGARDE          0.000000
FINANZTYP                 0.000000
ALTERSKATEGORIE_GROB      0.323264
GFK_URLAUBERTYP           0.544646
LP_STATUS_GROB            0.544646
LP_STATUS_FEIN            0.544646
ONLINE_AFFINITAET         0.544646
RETOURTYP_BK_S            0.544646
CJT_GESAMTTYP             0.544646
HH_EINKOMMEN_SCORE        2.058749
WOHNDAUER_2008            8.247000
ANZ_TITEL                 8.247000
SOHO_KZ                   8.247000
ANZ_PERSONEN              8.247000
KONSUMNAEHE               8.299737
LP_FAMILIE_GROB           8.728699
LP_FAMILIE_FEIN           8.728699
OST_WEST_KZ              10.451729
WOHNLAGE                 10.451729
GEBAEUDETYP              10.451729
MIN_GEBAEUDEJAHR         10.451729
GEBAEUDETYP_RASTER       10.452514
BALLRAUM                 10.518154
EWDICHTE                 10.518154
INNENSTADT               10.518154
LP_LEBENSPHASE_GROB      10.611509
ANZ_HH_TITEL             10.884842
ORTSGR_KLS9              10.914689
ARBEIT                   10.926022
RELAT_AB                 10.926022
LP_LEBENSPHASE_FEIN      10.954859
CAMEO_DEUG_2015          11.147852
CAMEO_DEU_2015           11.147852
CAMEO_INTL_2015          11.147852
ANZ_HAUSHALTE_AKTIV      11.176913
KBA13_ANZAHL_PKW         11.871354
PRAEGENDE_JUGENDJAHRE    12.136608
NATIONALITAET_KZ         12.153551
HEALTH_TYP               12.476816
VERS_TYP                 12.476816
SHOPPER_TYP              12.476816
PLZ8_ANTG2               13.073637
PLZ8_ANTG3               13.073637
PLZ8_ANTG1               13.073637
PLZ8_ANTG4               13.073637
PLZ8_BAUMAX              13.073637
PLZ8_HHZ                 13.073637
PLZ8_GBZ                 13.073637
KBA05_ANTG3              14.959701
KBA05_ANTG2              14.959701
KBA05_ANTG1              14.959701
MOBI_REGIO               14.959701
KBA05_GBZ                14.959701
KBA05_ANTG4              14.959701
W_KEIT_KIND_HH           16.605084
KKK                      17.735668
REGIOTYP                 17.735668
ALTER_HH                 34.813699
GEBURTSJAHR              44.020282
KBA05_BAUMAX             53.468668
KK_KUNDENTYP             65.596749
AGER_TYP                 76.955435
TITEL_KZ                 99.757636
dtype: float64
In [11]:
# Perform an assessment of how much missing data there is in each column of the
# dataset.
plt.figure(figsize=(20, 20))
sns.set_style("darkgrid")
missing_values = (azdias_demographics_data.isnull().sum()/azdias_demographics_data.shape[0]*100).sort_values(ascending=False).reset_index()
missing_values.columns = ['Column', 'Missing Values']
sns.barplot(data=missing_values, x='Column', y='Missing Values', hue='Column', palette='viridis', legend=False)
plt.title('Missing Values per Column', fontsize=14)
plt.xlabel('Columns', fontsize=12)
plt.ylabel('Count of Missing Values', fontsize=12)
plt.xticks(rotation=45, ha='right')  # Rotate column names for readability
plt.tight_layout()
No description has been provided for this image
In [12]:
azdias_demographics_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891221 entries, 0 to 891220
Data columns (total 85 columns):
 #   Column                 Non-Null Count   Dtype  
---  ------                 --------------   -----  
 0   AGER_TYP               205378 non-null  float64
 1   ALTERSKATEGORIE_GROB   888340 non-null  float64
 2   ANREDE_KZ              891221 non-null  float64
 3   CJT_GESAMTTYP          886367 non-null  float64
 4   FINANZ_MINIMALIST      891221 non-null  float64
 5   FINANZ_SPARER          891221 non-null  float64
 6   FINANZ_VORSORGER       891221 non-null  float64
 7   FINANZ_ANLEGER         891221 non-null  float64
 8   FINANZ_UNAUFFAELLIGER  891221 non-null  float64
 9   FINANZ_HAUSBAUER       891221 non-null  float64
 10  FINANZTYP              891221 non-null  float64
 11  GEBURTSJAHR            498903 non-null  float64
 12  GFK_URLAUBERTYP        886367 non-null  float64
 13  GREEN_AVANTGARDE       891221 non-null  float64
 14  HEALTH_TYP             780025 non-null  float64
 15  LP_LEBENSPHASE_FEIN    793589 non-null  float64
 16  LP_LEBENSPHASE_GROB    796649 non-null  float64
 17  LP_FAMILIE_FEIN        813429 non-null  float64
 18  LP_FAMILIE_GROB        813429 non-null  float64
 19  LP_STATUS_FEIN         886367 non-null  float64
 20  LP_STATUS_GROB         886367 non-null  float64
 21  NATIONALITAET_KZ       782906 non-null  float64
 22  PRAEGENDE_JUGENDJAHRE  783057 non-null  float64
 23  RETOURTYP_BK_S         886367 non-null  float64
 24  SEMIO_SOZ              891221 non-null  float64
 25  SEMIO_FAM              891221 non-null  float64
 26  SEMIO_REL              891221 non-null  float64
 27  SEMIO_MAT              891221 non-null  float64
 28  SEMIO_VERT             891221 non-null  float64
 29  SEMIO_LUST             891221 non-null  float64
 30  SEMIO_ERL              891221 non-null  float64
 31  SEMIO_KULT             891221 non-null  float64
 32  SEMIO_RAT              891221 non-null  float64
 33  SEMIO_KRIT             891221 non-null  float64
 34  SEMIO_DOM              891221 non-null  float64
 35  SEMIO_KAEM             891221 non-null  float64
 36  SEMIO_PFLICHT          891221 non-null  float64
 37  SEMIO_TRADV            891221 non-null  float64
 38  SHOPPER_TYP            780025 non-null  float64
 39  SOHO_KZ                817722 non-null  float64
 40  TITEL_KZ               2160 non-null    float64
 41  VERS_TYP               780025 non-null  float64
 42  ZABEOTYP               891221 non-null  float64
 43  ALTER_HH               580954 non-null  float64
 44  ANZ_PERSONEN           817722 non-null  float64
 45  ANZ_TITEL              817722 non-null  float64
 46  HH_EINKOMMEN_SCORE     872873 non-null  float64
 47  KK_KUNDENTYP           306609 non-null  float64
 48  W_KEIT_KIND_HH         743233 non-null  float64
 49  WOHNDAUER_2008         817722 non-null  float64
 50  ANZ_HAUSHALTE_AKTIV    791610 non-null  float64
 51  ANZ_HH_TITEL           794213 non-null  float64
 52  GEBAEUDETYP            798073 non-null  float64
 53  KONSUMNAEHE            817252 non-null  float64
 54  MIN_GEBAEUDEJAHR       798073 non-null  float64
 55  OST_WEST_KZ            798073 non-null  object 
 56  WOHNLAGE               798073 non-null  float64
 57  CAMEO_DEUG_2015        791869 non-null  object 
 58  CAMEO_DEU_2015         791869 non-null  object 
 59  CAMEO_INTL_2015        791869 non-null  object 
 60  KBA05_ANTG1            757897 non-null  float64
 61  KBA05_ANTG2            757897 non-null  float64
 62  KBA05_ANTG3            757897 non-null  float64
 63  KBA05_ANTG4            757897 non-null  float64
 64  KBA05_BAUMAX           414697 non-null  float64
 65  KBA05_GBZ              757897 non-null  float64
 66  BALLRAUM               797481 non-null  float64
 67  EWDICHTE               797481 non-null  float64
 68  INNENSTADT             797481 non-null  float64
 69  GEBAEUDETYP_RASTER     798066 non-null  float64
 70  KKK                    733157 non-null  float64
 71  MOBI_REGIO             757897 non-null  float64
 72  ONLINE_AFFINITAET      886367 non-null  float64
 73  REGIOTYP               733157 non-null  float64
 74  KBA13_ANZAHL_PKW       785421 non-null  float64
 75  PLZ8_ANTG1             774706 non-null  float64
 76  PLZ8_ANTG2             774706 non-null  float64
 77  PLZ8_ANTG3             774706 non-null  float64
 78  PLZ8_ANTG4             774706 non-null  float64
 79  PLZ8_BAUMAX            774706 non-null  float64
 80  PLZ8_HHZ               774706 non-null  float64
 81  PLZ8_GBZ               774706 non-null  float64
 82  ARBEIT                 793846 non-null  float64
 83  ORTSGR_KLS9            793947 non-null  float64
 84  RELAT_AB               793846 non-null  float64
dtypes: float64(81), object(4)
memory usage: 578.0+ MB
In [13]:
sns.distplot(nans_values)
plt.xlabel('percentage of missing values for each column')
plt.ylabel('Counts')
plt.title('Histogram for number of missing values')
plt.show()

/tmp/ipykernel_13/3719466440.py:1: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(nans_values)
No description has been provided for this image
In [14]:
# Investigate patterns in the amount of missing data in each column.
droping_columns = nans_values[nans_values>20].index
droping_columns
Out[14]:
Index(['ALTER_HH', 'GEBURTSJAHR', 'KBA05_BAUMAX', 'KK_KUNDENTYP', 'AGER_TYP',
       'TITEL_KZ'],
      dtype='object')
In [15]:
# Remove the outlier columns from the dataset. (You'll perform other data
# engineering tasks such as re-encoding and imputation later.)
azdias_demographics_data.drop(columns=droping_columns, axis=1, inplace=True)
In [16]:
azdias_demographics_data.head()
Out[16]:
ALTERSKATEGORIE_GROB ANREDE_KZ CJT_GESAMTTYP FINANZ_MINIMALIST FINANZ_SPARER FINANZ_VORSORGER FINANZ_ANLEGER FINANZ_UNAUFFAELLIGER FINANZ_HAUSBAUER FINANZTYP GFK_URLAUBERTYP GREEN_AVANTGARDE HEALTH_TYP LP_LEBENSPHASE_FEIN LP_LEBENSPHASE_GROB LP_FAMILIE_FEIN LP_FAMILIE_GROB LP_STATUS_FEIN LP_STATUS_GROB NATIONALITAET_KZ PRAEGENDE_JUGENDJAHRE RETOURTYP_BK_S SEMIO_SOZ SEMIO_FAM SEMIO_REL SEMIO_MAT SEMIO_VERT SEMIO_LUST SEMIO_ERL SEMIO_KULT SEMIO_RAT SEMIO_KRIT SEMIO_DOM SEMIO_KAEM SEMIO_PFLICHT SEMIO_TRADV SHOPPER_TYP SOHO_KZ VERS_TYP ZABEOTYP ANZ_PERSONEN ANZ_TITEL HH_EINKOMMEN_SCORE W_KEIT_KIND_HH WOHNDAUER_2008 ANZ_HAUSHALTE_AKTIV ANZ_HH_TITEL GEBAEUDETYP KONSUMNAEHE MIN_GEBAEUDEJAHR OST_WEST_KZ WOHNLAGE CAMEO_DEUG_2015 CAMEO_DEU_2015 CAMEO_INTL_2015 KBA05_ANTG1 KBA05_ANTG2 KBA05_ANTG3 KBA05_ANTG4 KBA05_GBZ BALLRAUM EWDICHTE INNENSTADT GEBAEUDETYP_RASTER KKK MOBI_REGIO ONLINE_AFFINITAET REGIOTYP KBA13_ANZAHL_PKW PLZ8_ANTG1 PLZ8_ANTG2 PLZ8_ANTG3 PLZ8_ANTG4 PLZ8_BAUMAX PLZ8_HHZ PLZ8_GBZ ARBEIT ORTSGR_KLS9 RELAT_AB
0 2.0 1.0 2.0 3.0 4.0 3.0 5.0 5.0 3.0 4.0 10.0 0.0 NaN 15.0 4.0 2.0 2.0 1.0 1.0 NaN NaN 5.0 2.0 6.0 7.0 5.0 1.0 5.0 3.0 3.0 4.0 7.0 6.0 6.0 5.0 3.0 NaN NaN NaN 3.0 NaN NaN 2.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 1.0 2.0 5.0 1.0 5.0 2.0 5.0 4.0 5.0 1.0 10.0 0.0 3.0 21.0 6.0 5.0 3.0 2.0 1.0 1.0 14.0 1.0 5.0 4.0 4.0 3.0 1.0 2.0 2.0 3.0 6.0 4.0 7.0 4.0 7.0 6.0 3.0 1.0 2.0 5.0 2.0 0.0 6.0 3.0 9.0 11.0 0.0 8.0 1.0 1992.0 W 4.0 8 8A 51 0.0 0.0 0.0 2.0 1.0 6.0 3.0 8.0 3.0 2.0 1.0 3.0 3.0 963.0 2.0 3.0 2.0 1.0 1.0 5.0 4.0 3.0 5.0 4.0
2 3.0 2.0 3.0 1.0 4.0 1.0 2.0 3.0 5.0 1.0 10.0 1.0 3.0 3.0 1.0 1.0 1.0 3.0 2.0 1.0 15.0 3.0 4.0 1.0 3.0 3.0 4.0 4.0 6.0 3.0 4.0 7.0 7.0 7.0 3.0 3.0 2.0 0.0 1.0 5.0 1.0 0.0 4.0 3.0 9.0 10.0 0.0 1.0 5.0 1992.0 W 2.0 4 4C 24 1.0 3.0 1.0 0.0 3.0 2.0 4.0 4.0 4.0 2.0 3.0 2.0 2.0 712.0 3.0 3.0 1.0 0.0 1.0 4.0 4.0 3.0 5.0 2.0
3 4.0 2.0 2.0 4.0 2.0 5.0 2.0 1.0 2.0 6.0 1.0 0.0 2.0 NaN NaN NaN NaN 9.0 4.0 1.0 8.0 2.0 5.0 1.0 2.0 1.0 4.0 4.0 7.0 4.0 3.0 4.0 4.0 5.0 4.0 4.0 1.0 0.0 1.0 3.0 0.0 0.0 1.0 NaN 9.0 1.0 0.0 1.0 4.0 1997.0 W 7.0 2 2A 12 4.0 1.0 0.0 0.0 4.0 4.0 2.0 6.0 4.0 NaN 4.0 1.0 NaN 596.0 2.0 2.0 2.0 0.0 1.0 3.0 4.0 2.0 3.0 3.0
4 3.0 1.0 5.0 4.0 3.0 4.0 1.0 3.0 2.0 5.0 5.0 0.0 3.0 32.0 10.0 10.0 5.0 3.0 2.0 1.0 8.0 5.0 6.0 4.0 4.0 2.0 7.0 4.0 4.0 6.0 2.0 3.0 2.0 2.0 4.0 2.0 2.0 0.0 2.0 4.0 4.0 0.0 5.0 2.0 9.0 3.0 0.0 1.0 4.0 1992.0 W 3.0 6 6B 43 1.0 4.0 1.0 0.0 3.0 2.0 5.0 1.0 5.0 3.0 3.0 5.0 5.0 435.0 2.0 4.0 2.0 1.0 2.0 3.0 3.0 4.0 6.0 5.0
In [17]:
azdias_demographics_data.shape
Out[17]:
(891221, 79)

Discussion 1.1.2: Assess Missing Data in Each Column¶

From The histogram plot and barplot there are 6 columns that are outliers ['ALTER_HH', 'GEBURTSJAHR', 'KBA05_BAUMAX', 'KK_KUNDENTYP', 'AGER_TYP','TITEL_KZ'] These columns have more than 20% of missing data

Step 1.1.3: Assess Missing Data in Each Row¶

Now, you'll perform a similar assessment for the rows of the dataset. How much data is missing in each row? As with the columns, you should see some groups of points that have a very different numbers of missing values. Divide the data into two subsets: one for data points that are above some threshold for missing values, and a second subset for points below that threshold.

In order to know what to do with the outlier rows, we should see if the distribution of data values on columns that are not missing data (or are missing very little data) are similar or different between the two groups. Select at least five of these columns and compare the distribution of values.

  • You can use seaborn's countplot() function to create a bar chart of code frequencies and matplotlib's subplot() function to put bar charts for the two subplots side by side.
  • To reduce repeated code, you might want to write a function that can perform this comparison, taking as one of its arguments a column to be compared.

Depending on what you observe in your comparison, this will have implications on how you approach your conclusions later in the analysis. If the distributions of non-missing features look similar between the data with many missing values and the data with few or no missing values, then we could argue that simply dropping those points from the analysis won't present a major issue. On the other hand, if the data with many missing values looks very different from the data with few or no missing values, then we should make a note on those data as special. We'll revisit these data later on. Either way, you should continue your analysis for now using just the subset of the data with few or no missing values.

In [18]:
# How much data is missing in each row of the dataset?
nans_rows_values = azdias_demographics_data.isnull().sum(axis=1)
nans_rows_values.describe()
Out[18]:
count    891221.000000
mean          5.649894
std          13.234687
min           0.000000
25%           0.000000
50%           0.000000
75%           3.000000
max          49.000000
dtype: float64
In [19]:
sns.distplot(nans_rows_values, 
            bins=25)
plt.xlabel('percentage of missing values for each Row')
plt.ylabel('Counts')
plt.title('Histogram for number of missing values')
plt.show()

/tmp/ipykernel_13/3930819879.py:1: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(nans_rows_values,
No description has been provided for this image
In [20]:
# Write code to divide the data into two subsets based on the number of missing
# values in each row.
missing_values_below_threshold = azdias_demographics_data[nans_rows_values<20]
missing_values_above_threshold = azdias_demographics_data[nans_rows_values>=20]
In [21]:
nans_values = nans_values[nans_values>0]
nans_values
Out[21]:
ALTERSKATEGORIE_GROB      0.323264
GFK_URLAUBERTYP           0.544646
LP_STATUS_GROB            0.544646
LP_STATUS_FEIN            0.544646
ONLINE_AFFINITAET         0.544646
RETOURTYP_BK_S            0.544646
CJT_GESAMTTYP             0.544646
HH_EINKOMMEN_SCORE        2.058749
WOHNDAUER_2008            8.247000
ANZ_TITEL                 8.247000
SOHO_KZ                   8.247000
ANZ_PERSONEN              8.247000
KONSUMNAEHE               8.299737
LP_FAMILIE_GROB           8.728699
LP_FAMILIE_FEIN           8.728699
OST_WEST_KZ              10.451729
WOHNLAGE                 10.451729
GEBAEUDETYP              10.451729
MIN_GEBAEUDEJAHR         10.451729
GEBAEUDETYP_RASTER       10.452514
BALLRAUM                 10.518154
EWDICHTE                 10.518154
INNENSTADT               10.518154
LP_LEBENSPHASE_GROB      10.611509
ANZ_HH_TITEL             10.884842
ORTSGR_KLS9              10.914689
ARBEIT                   10.926022
RELAT_AB                 10.926022
LP_LEBENSPHASE_FEIN      10.954859
CAMEO_DEUG_2015          11.147852
CAMEO_DEU_2015           11.147852
CAMEO_INTL_2015          11.147852
ANZ_HAUSHALTE_AKTIV      11.176913
KBA13_ANZAHL_PKW         11.871354
PRAEGENDE_JUGENDJAHRE    12.136608
NATIONALITAET_KZ         12.153551
HEALTH_TYP               12.476816
VERS_TYP                 12.476816
SHOPPER_TYP              12.476816
PLZ8_ANTG2               13.073637
PLZ8_ANTG3               13.073637
PLZ8_ANTG1               13.073637
PLZ8_ANTG4               13.073637
PLZ8_BAUMAX              13.073637
PLZ8_HHZ                 13.073637
PLZ8_GBZ                 13.073637
KBA05_ANTG3              14.959701
KBA05_ANTG2              14.959701
KBA05_ANTG1              14.959701
MOBI_REGIO               14.959701
KBA05_GBZ                14.959701
KBA05_ANTG4              14.959701
W_KEIT_KIND_HH           16.605084
KKK                      17.735668
REGIOTYP                 17.735668
ALTER_HH                 34.813699
GEBURTSJAHR              44.020282
KBA05_BAUMAX             53.468668
KK_KUNDENTYP             65.596749
AGER_TYP                 76.955435
TITEL_KZ                 99.757636
dtype: float64
In [22]:
nans_columns= nans_values[nans_values<20].index
top_few_missing_cols=nans_columns[:5]
top_few_missing_cols
Out[22]:
Index(['ALTERSKATEGORIE_GROB', 'GFK_URLAUBERTYP', 'LP_STATUS_GROB',
       'LP_STATUS_FEIN', 'ONLINE_AFFINITAET'],
      dtype='object')
In [23]:
def create_plot(column, df_below_thres, df_above_thres, max_categories=20):
    # Precompute value counts for the column in both subsets
    counts_below_thres = df_below_thres[column].value_counts().head(max_categories)
    counts_above_thres = df_above_thres[column].value_counts().head(max_categories)

    # Create figure with two subplots
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))

    # Plot for missing values less than 20
    sns.barplot(x=counts_below_thres.index, y=counts_below_thres.values, ax=ax1, color='skyblue')
    ax1.set_title('Missing Values < 20', fontsize=10)
    ax1.set_xlabel(column, fontsize=8)
    ax1.set_ylabel('Count', fontsize=8)
    ax1.tick_params(axis='x', rotation=45, labelsize=7)

    # Plot for missing values above 20
    sns.barplot(x=counts_above_thres.index, y=counts_above_thres.values, ax=ax2, color='salmon')
    ax2.set_title('Missing Values >= 20', fontsize=10)
    ax2.set_xlabel(column, fontsize=8)
    ax2.set_ylabel('Count', fontsize=8)
    ax2.tick_params(axis='x', rotation=45, labelsize=7)

    # Adjust layout and show
    plt.tight_layout()
    plt.show()

# Precompute subsets (do this once outside the function)
# Assuming missing_less_25 and missing_above_25 are index-based filters
df_less_20 = azdias_demographics_data.loc[missing_values_below_threshold.index]
df_above_20 = azdias_demographics_data.loc[missing_values_above_threshold.index]
In [24]:
# Compare the distribution of values for at least five columns where there are
# no or few missing values, between the two subsets.
for i in range(5):
    create_plot(top_few_missing_cols[i],df_less_20, df_above_20)
No description has been provided for this image
No description has been provided for this image
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Discussion 1.1.3: Assess Missing Data in Each Row¶

Based on the histogram and the bar plot we observed that the counts of the unique values for the 5 columns we selected, is much more when the missing number in a row is less than 20 than when the missing number of rows is more than 20; which means that the missing numbers is more when we have missing number of rows greater than 20. So we continue with the df_above_20

In [25]:
df_less_20 = df_less_20.fillna(df_less_20.mode().iloc[0]) # fill the missing values with the most frequent value
In [26]:
df_less_20.describe()
Out[26]:
ALTERSKATEGORIE_GROB ANREDE_KZ CJT_GESAMTTYP FINANZ_MINIMALIST FINANZ_SPARER FINANZ_VORSORGER FINANZ_ANLEGER FINANZ_UNAUFFAELLIGER FINANZ_HAUSBAUER FINANZTYP GFK_URLAUBERTYP GREEN_AVANTGARDE HEALTH_TYP LP_LEBENSPHASE_FEIN LP_LEBENSPHASE_GROB LP_FAMILIE_FEIN LP_FAMILIE_GROB LP_STATUS_FEIN LP_STATUS_GROB NATIONALITAET_KZ PRAEGENDE_JUGENDJAHRE RETOURTYP_BK_S SEMIO_SOZ SEMIO_FAM SEMIO_REL SEMIO_MAT SEMIO_VERT SEMIO_LUST SEMIO_ERL SEMIO_KULT SEMIO_RAT SEMIO_KRIT SEMIO_DOM SEMIO_KAEM SEMIO_PFLICHT SEMIO_TRADV SHOPPER_TYP SOHO_KZ VERS_TYP ZABEOTYP ANZ_PERSONEN ANZ_TITEL HH_EINKOMMEN_SCORE W_KEIT_KIND_HH WOHNDAUER_2008 ANZ_HAUSHALTE_AKTIV ANZ_HH_TITEL GEBAEUDETYP KONSUMNAEHE MIN_GEBAEUDEJAHR WOHNLAGE KBA05_ANTG1 KBA05_ANTG2 KBA05_ANTG3 KBA05_ANTG4 KBA05_GBZ BALLRAUM EWDICHTE INNENSTADT GEBAEUDETYP_RASTER KKK MOBI_REGIO ONLINE_AFFINITAET REGIOTYP KBA13_ANZAHL_PKW PLZ8_ANTG1 PLZ8_ANTG2 PLZ8_ANTG3 PLZ8_ANTG4 PLZ8_BAUMAX PLZ8_HHZ PLZ8_GBZ ARBEIT ORTSGR_KLS9 RELAT_AB
count 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.00000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000 797077.000000
mean 2.795889 1.521377 3.505508 3.058727 2.715815 3.432951 2.840685 2.656884 3.115607 3.799344 7.497141 0.219722 2.235834 15.360383 4.774415 3.798516 2.323862 4.768006 2.451369 1.161456 9.447494 3.451732 4.14123 4.113152 3.993661 3.886485 4.277446 4.334015 4.622254 4.132843 3.893664 4.548362 4.554295 4.294187 4.184349 3.725147 1.551983 0.008414 1.540041 3.379054 1.729775 0.004151 4.415189 4.297359 7.912239 8.300877 0.040475 2.799820 3.022634 1993.257848 4.054119 1.420801 1.203356 0.593825 0.290888 3.150757 4.154264 3.941083 4.549493 3.738190 2.745691 2.866965 2.740258 4.595157 631.470423 2.246211 2.807429 1.606795 0.679538 1.917420 3.595612 3.370378 3.171405 5.292297 3.070929
std 1.016826 0.499543 1.533402 1.377570 1.485032 1.376959 1.472527 1.399118 1.407806 2.084767 3.580786 0.414058 0.756382 12.401118 3.697161 3.900154 1.687275 3.522233 1.511468 0.465864 4.049160 1.455128 1.94108 1.913484 1.910233 1.913611 1.944819 2.103057 1.826708 1.958003 1.653117 1.759666 1.826313 1.867894 1.854900 1.765480 1.009655 0.091344 0.498394 1.407834 1.156330 0.068529 1.545029 1.777945 1.920547 15.631775 0.323412 2.657595 1.550727 3.282927 1.947922 1.406629 1.244640 0.997401 0.626330 1.296881 2.183554 1.719255 2.028286 0.923085 0.942608 1.456553 1.554415 1.809272 350.634939 0.959177 0.907885 0.975076 0.726103 1.447445 0.965521 1.097687 0.998218 2.297430 1.356907
min 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.000000 0.000000 1.000000 1.000000 0.000000 0.000000 1.000000 1.000000 1.000000 1.000000 0.000000 1.000000 1.000000 1985.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
25% 2.000000 1.000000 2.000000 2.000000 1.000000 2.000000 1.000000 1.000000 2.000000 2.000000 4.000000 0.000000 2.000000 5.000000 2.000000 1.000000 1.000000 1.000000 1.000000 1.000000 6.000000 2.000000 2.00000 2.000000 3.000000 2.000000 2.000000 2.000000 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000 2.000000 1.000000 0.000000 1.000000 3.000000 1.000000 0.000000 3.000000 3.000000 8.000000 1.000000 0.000000 1.000000 2.000000 1992.000000 3.000000 0.000000 0.000000 0.000000 0.000000 2.000000 2.000000 2.000000 3.000000 3.000000 2.000000 1.000000 1.000000 3.000000 386.000000 2.000000 2.000000 1.000000 0.000000 1.000000 3.000000 3.000000 3.000000 4.000000 2.000000
50% 3.000000 2.000000 4.000000 3.000000 3.000000 4.000000 3.000000 2.000000 3.000000 4.000000 8.000000 0.000000 2.000000 12.000000 3.000000 1.000000 1.000000 4.000000 2.000000 1.000000 10.000000 4.000000 4.00000 4.000000 4.000000 4.000000 5.000000 5.000000 4.000000 4.000000 4.000000 5.000000 5.000000 4.000000 4.000000 4.000000 1.000000 0.000000 2.000000 3.000000 1.000000 0.000000 5.000000 5.000000 9.000000 4.000000 0.000000 1.000000 3.000000 1992.000000 3.000000 1.000000 1.000000 0.000000 0.000000 3.000000 5.000000 4.000000 5.000000 4.000000 3.000000 3.000000 3.000000 5.000000 554.000000 2.000000 3.000000 2.000000 1.000000 1.000000 3.000000 3.000000 3.000000 5.000000 3.000000
75% 4.000000 2.000000 5.000000 4.000000 4.000000 5.000000 4.000000 4.000000 4.000000 6.000000 11.000000 0.000000 3.000000 27.000000 8.000000 8.000000 4.000000 9.000000 4.000000 1.000000 14.000000 5.000000 6.00000 6.000000 5.000000 5.000000 6.000000 6.000000 6.000000 6.000000 5.000000 6.000000 6.000000 6.000000 6.000000 5.000000 2.000000 0.000000 2.000000 4.000000 2.000000 0.000000 6.000000 6.000000 9.000000 9.000000 0.000000 3.000000 4.000000 1993.000000 5.000000 3.000000 2.000000 1.000000 0.000000 4.000000 6.000000 6.000000 6.000000 4.000000 3.000000 4.000000 4.000000 6.000000 793.000000 3.000000 3.000000 2.000000 1.000000 3.000000 4.000000 4.000000 4.000000 7.000000 4.000000
max 4.000000 2.000000 6.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 6.000000 12.000000 1.000000 3.000000 40.000000 12.000000 11.000000 5.000000 10.000000 5.000000 3.000000 15.000000 5.000000 7.00000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 3.000000 1.000000 2.000000 6.000000 45.000000 4.000000 6.000000 6.000000 9.000000 595.000000 23.000000 8.000000 7.000000 2016.000000 8.000000 4.000000 4.000000 3.000000 2.000000 5.000000 7.000000 6.000000 8.000000 5.000000 4.000000 6.000000 5.000000 7.000000 2300.000000 4.000000 4.000000 3.000000 2.000000 5.000000 5.000000 5.000000 5.000000 9.000000 5.000000

Step 1.2: Select and Re-Encode Features¶

Checking for missing data isn't the only way in which you can prepare a dataset for analysis. Since the unsupervised learning techniques to be used will only work on data that is encoded numerically, you need to make a few encoding changes or additional assumptions to be able to make progress. In addition, while almost all of the values in the dataset are encoded using numbers, not all of them represent numeric values. Check the third column of the feature summary (feat_info) for a summary of types of measurement.

  • For numeric and interval data, these features can be kept without changes.
  • Most of the variables in the dataset are ordinal in nature. While ordinal values may technically be non-linear in spacing, make the simplifying assumption that the ordinal variables can be treated as being interval in nature (that is, kept without any changes).
  • Special handling may be necessary for the remaining two variable types: categorical, and 'mixed'.

In the first two parts of this sub-step, you will perform an investigation of the categorical and mixed-type features and make a decision on each of them, whether you will keep, drop, or re-encode each. Then, in the last part, you will create a new data frame with only the selected and engineered columns.

Data wrangling is often the trickiest part of the data analysis process, and there's a lot of it to be done here. But stick with it: once you're done with this step, you'll be ready to get to the machine learning parts of the project!

In [27]:
azdias_feature_summary.head()
Out[27]:
attribute information_level type missing_or_unknown
0 AGER_TYP person categorical [-1,0]
1 ALTERSKATEGORIE_GROB person ordinal [-1,0,9]
2 ANREDE_KZ person categorical [-1,0]
3 CJT_GESAMTTYP person categorical [0]
4 FINANZ_MINIMALIST person ordinal [-1]
In [28]:
# How many features are there of each data type?
feat_info = azdias_feature_summary[azdias_feature_summary['attribute'].isin(df_less_20.columns)]
feat_info['type'].value_counts()
Out[28]:
type
ordinal        49
categorical    18
mixed           6
numeric         6
Name: count, dtype: int64

Step 1.2.1: Re-Encode Categorical Features¶

For categorical data, you would ordinarily need to encode the levels as dummy variables. Depending on the number of categories, perform one of the following:

  • For binary (two-level) categoricals that take numeric values, you can keep them without needing to do anything.
  • There is one binary variable that takes on non-numeric values. For this one, you need to re-encode the values as numbers or create a dummy variable.
  • For multi-level categoricals (three or more values), you can choose to encode the values using multiple dummy variables (e.g. via OneHotEncoder), or (to keep things straightforward) just drop them from the analysis. As always, document your choices in the Discussion section.
In [29]:
categorical_data = feat_info[feat_info['type'] == "categorical"]
In [30]:
categories_cols= categorical_data.attribute
In [31]:
categories_cols.iloc[0]
Out[31]:
'ANREDE_KZ'
In [32]:
# Assess categorical variables: which are binary, which are multi-level, and
# which one needs to be re-encoded?
binary_categoricals = []
multi_class_categoricals = []
for column in categories_cols:
    if azdias_demographics_data[column].nunique() == 2:
        binary_categoricals.append(column)
    else:
        multi_class_categoricals.append(column)
In [33]:
print("Number of Binary Class Categorical Data {} VS Multi Class Categorical Data {}".format(len(binary_categoricals), len(multi_class_categoricals)))

Number of Binary Class Categorical Data 5 VS Multi Class Categorical Data 13
In [34]:
df_less_20[binary_categoricals].value_counts()
Out[34]:
ANREDE_KZ  GREEN_AVANTGARDE  SOHO_KZ  VERS_TYP  OST_WEST_KZ
2.0        0.0               0.0      2.0       W              130472
1.0        0.0               0.0      2.0       W              130294
2.0        0.0               0.0      1.0       W              121795
1.0        0.0               0.0      1.0       W               84506
           1.0               0.0      2.0       W               41702
                                      1.0       W               41183
2.0        0.0               0.0      1.0       O               40887
1.0        0.0               0.0      2.0       O               39417
2.0        1.0               0.0      2.0       W               39373
           0.0               0.0      2.0       O               37928
           1.0               0.0      1.0       W               33968
1.0        0.0               0.0      1.0       O               31516
           1.0               0.0      1.0       O                5469
2.0        1.0               0.0      1.0       O                4171
1.0        1.0               0.0      2.0       O                4153
2.0        1.0               0.0      2.0       O                3536
1.0        0.0               1.0      2.0       W                1122
2.0        0.0               1.0      2.0       W                1092
                                      1.0       W                1020
1.0        0.0               1.0      1.0       W                 742
           1.0               1.0      1.0       W                 383
2.0        1.0               1.0      2.0       W                 364
1.0        1.0               1.0      2.0       W                 359
2.0        1.0               1.0      1.0       W                 320
           0.0               1.0      1.0       O                 309
1.0        0.0               1.0      2.0       O                 288
2.0        0.0               1.0      2.0       O                 286
1.0        0.0               1.0      1.0       O                 268
           1.0               1.0      1.0       O                  53
                                      2.0       O                  44
2.0        1.0               1.0      1.0       O                  33
                                      2.0       O                  24
Name: count, dtype: int64
In [35]:
for col in binary_categoricals:
    print(df_less_20[col].value_counts(), "\n")
    print("="*30)

ANREDE_KZ
2.0    415578
1.0    381499
Name: count, dtype: int64 

==============================
GREEN_AVANTGARDE
0.0    621942
1.0    175135
Name: count, dtype: int64 

==============================
SOHO_KZ
0.0    790370
1.0      6707
Name: count, dtype: int64 

==============================
VERS_TYP
2.0    430454
1.0    366623
Name: count, dtype: int64 

==============================
OST_WEST_KZ
W    628695
O    168382
Name: count, dtype: int64 

==============================
In [36]:
OST_WEST_KZ_mappibg = {'W':0, 'O':1}
VERS_TYP_ANREDE_KZ_mapping = {1:1, 2:0}
int_mapping = {0.0:0, 1.0:1}
df_less_20['OST_WEST_KZ'] = df_less_20['OST_WEST_KZ'].map(OST_WEST_KZ_mappibg)
df_less_20['VERS_TYP'] = df_less_20['VERS_TYP'].map(VERS_TYP_ANREDE_KZ_mapping)
df_less_20['ANREDE_KZ'] = df_less_20['ANREDE_KZ'].map(VERS_TYP_ANREDE_KZ_mapping)
df_less_20['SOHO_KZ'] = df_less_20['SOHO_KZ'].map(int_mapping)
df_less_20['GREEN_AVANTGARDE'] = df_less_20['GREEN_AVANTGARDE'].map(int_mapping)
df_less_20.head()
Out[36]:
ALTERSKATEGORIE_GROB ANREDE_KZ CJT_GESAMTTYP FINANZ_MINIMALIST FINANZ_SPARER FINANZ_VORSORGER FINANZ_ANLEGER FINANZ_UNAUFFAELLIGER FINANZ_HAUSBAUER FINANZTYP GFK_URLAUBERTYP GREEN_AVANTGARDE HEALTH_TYP LP_LEBENSPHASE_FEIN LP_LEBENSPHASE_GROB LP_FAMILIE_FEIN LP_FAMILIE_GROB LP_STATUS_FEIN LP_STATUS_GROB NATIONALITAET_KZ PRAEGENDE_JUGENDJAHRE RETOURTYP_BK_S SEMIO_SOZ SEMIO_FAM SEMIO_REL SEMIO_MAT SEMIO_VERT SEMIO_LUST SEMIO_ERL SEMIO_KULT SEMIO_RAT SEMIO_KRIT SEMIO_DOM SEMIO_KAEM SEMIO_PFLICHT SEMIO_TRADV SHOPPER_TYP SOHO_KZ VERS_TYP ZABEOTYP ANZ_PERSONEN ANZ_TITEL HH_EINKOMMEN_SCORE W_KEIT_KIND_HH WOHNDAUER_2008 ANZ_HAUSHALTE_AKTIV ANZ_HH_TITEL GEBAEUDETYP KONSUMNAEHE MIN_GEBAEUDEJAHR OST_WEST_KZ WOHNLAGE CAMEO_DEUG_2015 CAMEO_DEU_2015 CAMEO_INTL_2015 KBA05_ANTG1 KBA05_ANTG2 KBA05_ANTG3 KBA05_ANTG4 KBA05_GBZ BALLRAUM EWDICHTE INNENSTADT GEBAEUDETYP_RASTER KKK MOBI_REGIO ONLINE_AFFINITAET REGIOTYP KBA13_ANZAHL_PKW PLZ8_ANTG1 PLZ8_ANTG2 PLZ8_ANTG3 PLZ8_ANTG4 PLZ8_BAUMAX PLZ8_HHZ PLZ8_GBZ ARBEIT ORTSGR_KLS9 RELAT_AB
1 1.0 0 5.0 1.0 5.0 2.0 5.0 4.0 5.0 1.0 10.0 0 3.0 21.0 6.0 5.0 3.0 2.0 1.0 1.0 14.0 1.0 5.0 4.0 4.0 3.0 1.0 2.0 2.0 3.0 6.0 4.0 7.0 4.0 7.0 6.0 3.0 1 0 5.0 2.0 0.0 6.0 3.0 9.0 11.0 0.0 8.0 1.0 1992.0 0 4.0 8 8A 51 0.0 0.0 0.0 2.0 1.0 6.0 3.0 8.0 3.0 2.0 1.0 3.0 3.0 963.0 2.0 3.0 2.0 1.0 1.0 5.0 4.0 3.0 5.0 4.0
2 3.0 0 3.0 1.0 4.0 1.0 2.0 3.0 5.0 1.0 10.0 1 3.0 3.0 1.0 1.0 1.0 3.0 2.0 1.0 15.0 3.0 4.0 1.0 3.0 3.0 4.0 4.0 6.0 3.0 4.0 7.0 7.0 7.0 3.0 3.0 2.0 0 1 5.0 1.0 0.0 4.0 3.0 9.0 10.0 0.0 1.0 5.0 1992.0 0 2.0 4 4C 24 1.0 3.0 1.0 0.0 3.0 2.0 4.0 4.0 4.0 2.0 3.0 2.0 2.0 712.0 3.0 3.0 1.0 0.0 1.0 4.0 4.0 3.0 5.0 2.0
3 4.0 0 2.0 4.0 2.0 5.0 2.0 1.0 2.0 6.0 1.0 0 2.0 1.0 2.0 1.0 1.0 9.0 4.0 1.0 8.0 2.0 5.0 1.0 2.0 1.0 4.0 4.0 7.0 4.0 3.0 4.0 4.0 5.0 4.0 4.0 1.0 0 1 3.0 0.0 0.0 1.0 6.0 9.0 1.0 0.0 1.0 4.0 1997.0 0 7.0 2 2A 12 4.0 1.0 0.0 0.0 4.0 4.0 2.0 6.0 4.0 3.0 4.0 1.0 6.0 596.0 2.0 2.0 2.0 0.0 1.0 3.0 4.0 2.0 3.0 3.0
4 3.0 1 5.0 4.0 3.0 4.0 1.0 3.0 2.0 5.0 5.0 0 3.0 32.0 10.0 10.0 5.0 3.0 2.0 1.0 8.0 5.0 6.0 4.0 4.0 2.0 7.0 4.0 4.0 6.0 2.0 3.0 2.0 2.0 4.0 2.0 2.0 0 0 4.0 4.0 0.0 5.0 2.0 9.0 3.0 0.0 1.0 4.0 1992.0 0 3.0 6 6B 43 1.0 4.0 1.0 0.0 3.0 2.0 5.0 1.0 5.0 3.0 3.0 5.0 5.0 435.0 2.0 4.0 2.0 1.0 2.0 3.0 3.0 4.0 6.0 5.0
5 1.0 0 2.0 3.0 1.0 5.0 2.0 2.0 5.0 2.0 1.0 0 3.0 8.0 2.0 1.0 1.0 4.0 2.0 1.0 3.0 3.0 2.0 4.0 7.0 4.0 2.0 2.0 2.0 5.0 7.0 4.0 4.0 4.0 7.0 6.0 0.0 0 0 4.0 1.0 0.0 5.0 6.0 9.0 5.0 0.0 1.0 5.0 1992.0 0 7.0 8 8C 54 2.0 2.0 0.0 0.0 4.0 6.0 2.0 7.0 4.0 4.0 4.0 1.0 5.0 1300.0 2.0 3.0 1.0 1.0 1.0 5.0 5.0 2.0 3.0 3.0
In [37]:
for col in multi_class_categoricals:
    print(df_less_20[col].value_counts(), "\n")
    print("="*30)

CJT_GESAMTTYP
4.0    202721
3.0    147068
2.0    141166
5.0    111032
6.0    101898
1.0     93192
Name: count, dtype: int64 

==============================
FINANZTYP
6.0    289004
1.0    196805
5.0    106220
2.0    104577
4.0     55874
3.0     44597
Name: count, dtype: int64 

==============================
GFK_URLAUBERTYP
12.0    134615
10.0    102748
8.0      82992
11.0     75051
5.0      70468
4.0      60413
9.0      57046
3.0      53094
1.0      50640
2.0      43647
7.0      40642
6.0      25721
Name: count, dtype: int64 

==============================
LP_FAMILIE_FEIN
1.0     433230
10.0    128902
2.0      98491
11.0     48727
8.0      21777
7.0      19568
4.0      11573
5.0      11164
9.0      10451
6.0       8512
3.0       4682
Name: count, dtype: int64 

==============================
LP_FAMILIE_GROB
1.0    433230
5.0    188080
2.0     98491
4.0     49857
3.0     27419
Name: count, dtype: int64 

==============================
LP_STATUS_FEIN
1.0     211398
9.0     136229
10.0    111538
2.0     111016
4.0      73938
3.0      68893
6.0      28870
5.0      27472
8.0      18525
7.0       9198
Name: count, dtype: int64 

==============================
LP_STATUS_GROB
1.0    322414
2.0    170303
4.0    154754
5.0    111538
3.0     38068
Name: count, dtype: int64 

==============================
NATIONALITAET_KZ
1.0    700921
2.0     63619
3.0     32537
Name: count, dtype: int64 

==============================
SHOPPER_TYP
1.0    283490
2.0    205874
3.0    180604
0.0    127109
Name: count, dtype: int64 

==============================
ZABEOTYP
3.0    281772
4.0    207383
1.0    123270
5.0     80892
6.0     70817
2.0     32943
Name: count, dtype: int64 

==============================
GEBAEUDETYP
1.0    459844
3.0    178507
8.0    152439
2.0      4789
4.0       885
6.0       612
5.0         1
Name: count, dtype: int64 

==============================
CAMEO_DEUG_2015
8    140261
9    108138
6    105819
4    103814
3     86612
2     83149
7     77888
5     55216
1     36180
Name: count, dtype: int64 

==============================
CAMEO_DEU_2015
6B    62509
8A    52427
4C    47765
2D    35047
3C    34740
7A    34384
3D    34275
8B    33424
4A    33128
8C    30978
9D    28591
9B    27661
9C    24986
7B    24489
9A    20537
2C    19408
8D    17565
6E    16104
2B    15468
5D    14934
6C    14815
2A    13226
5A    12153
1D    11908
1A    10837
3A    10454
5B    10345
5C     9926
7C     9059
4B     9038
4D     8565
3B     7143
6A     6799
9E     6363
6D     6068
6F     5391
7D     5329
4E     5318
1E     5057
7E     4627
1C     4310
5F     4281
1B     4068
5E     3577
Name: count, dtype: int64 

==============================
In [38]:
# Re-encode categorical variable(s) to be kept in the analysis.
# df_less_25=pd.get_dummies(data=df_less_25,columns=multi_class_categoricals, drop_first=True)
print(df_less_20.shape)

(797077, 79)

Discussion 1.2.1: Re-Encode Categorical Features¶

Used pandas get_dummies function to encode multi class categorical variables, and for the binary variables (numerical and non-numerical variables) used a manual map dictionary

Step 1.2.2: Engineer Mixed-Type Features¶

There are a handful of features that are marked as "mixed" in the feature summary that require special treatment in order to be included in the analysis. There are two in particular that deserve attention; the handling of the rest are up to your own choices:

  • "PRAEGENDE_JUGENDJAHRE" combines information on three dimensions: generation by decade, movement (mainstream vs. avantgarde), and nation (east vs. west). While there aren't enough levels to disentangle east from west, you should create two new variables to capture the other two dimensions: an interval-type variable for decade, and a binary variable for movement.
  • "CAMEO_INTL_2015" combines information on two axes: wealth and life stage. Break up the two-digit codes by their 'tens'-place and 'ones'-place digits into two new ordinal variables (which, for the purposes of this project, is equivalent to just treating them as their raw numeric values).
  • If you decide to keep or engineer new features around the other mixed-type features, make sure you note your steps in the Discussion section.

Be sure to check Data_Dictionary.md for the details needed to finish these tasks.

  • PRAEGENDE_JUGENDJAHRE
    • Dominating movement of person's youth (avantgarde vs. mainstream; east vs. west)
      • -1: unknown
      • 0: unknown
      • 1: 40s - war years (Mainstream, E+W)
      • 2: 40s - reconstruction years (Avantgarde, E+W)
      • 3: 50s - economic miracle (Mainstream, E+W)
      • 4: 50s - milk bar / Individualisation (Avantgarde, E+W)
      • 5: 60s - economic miracle (Mainstream, E+W)
      • 6: 60s - generation 68 / student protestors (Avantgarde, W)
      • 7: 60s - opponents to the building of the Wall (Avantgarde, E)
      • 8: 70s - family orientation (Mainstream, E+W)
      • 9: 70s - peace movement (Avantgarde, E+W)
      • 10: 80s - Generation Golf (Mainstream, W)
      • 11: 80s - ecological awareness (Avantgarde, W)
      • 12: 80s - FDJ / communist party youth organisation (Mainstream, E)
      • 13: 80s - Swords into ploughshares (Avantgarde, E)
      • 14: 90s - digital media kids (Mainstream, E+W)
      • 15: 90s - ecological awareness (Avantgarde, E+W)
In [39]:
# Investigate "PRAEGENDE_JUGENDJAHRE" and engineer two new variables.
mapping_decades = {
    1:1,2:1,3:2,4:2,5:3,
    6:3,7:3,8:4,9:4,10:5,
    11:5,12:5,13:5,14:6,15:6
}
mapping_movement={
    1:1,2:0,3:1,4:0,5:1,
    6:0,7:0,8:1,9:0,10:1,
    11:0,12:1,13:0,14:1,15:0 
}
df_less_20['DECADE'] = df_less_20['PRAEGENDE_JUGENDJAHRE']
df_less_20['MOVEMENT'] = df_less_20['PRAEGENDE_JUGENDJAHRE']

df_less_20['DECADE'] = df_less_20['DECADE'].map(mapping_decades)
df_less_20['MOVEMENT'] = df_less_20['MOVEMENT'].map(mapping_movement)
df_less_20.drop(columns='PRAEGENDE_JUGENDJAHRE', axis=1, inplace=True)
  • CAMEO_INTL_2015
    • German CAMEO: Wealth / Life Stage Typology, mapped to international code
      • -1: unknown
      • 11: Wealthy Households - Pre-Family Couples & Singles
      • 12: Wealthy Households - Young Couples With Children
      • 13: Wealthy Households - Families With School Age Children
      • 14: Wealthy Households - Older Families & Mature Couples
      • 15: Wealthy Households - Elders In Retirement
      • 21: Prosperous Households - Pre-Family Couples & Singles
      • 22: Prosperous Households - Young Couples With Children
      • 23: Prosperous Households - Families With School Age Children
      • 24: Prosperous Households - Older Families & Mature Couples
      • 25: Prosperous Households - Elders In Retirement
      • 31: Comfortable Households - Pre-Family Couples & Singles
      • 32: Comfortable Households - Young Couples With Children
      • 33: Comfortable Households - Families With School Age Children
      • 34: Comfortable Households - Older Families & Mature Couples
      • 35: Comfortable Households - Elders In Retirement
      • 41: Less Affluent Households - Pre-Family Couples & Singles
      • 42: Less Affluent Households - Young Couples With Children
      • 43: Less Affluent Households - Families With School Age Children
      • 44: Less Affluent Households - Older Families & Mature Couples
      • 45: Less Affluent Households - Elders In Retirement
      • 51: Poorer Households - Pre-Family Couples & Singles
      • 52: Poorer Households - Young Couples With Children
      • 53: Poorer Households - Families With School Age Children
      • 54: Poorer Households - Older Families & Mature Couples
      • 55: Poorer Households - Elders In Retirement
      • XX: unknown
In [40]:
type(df_less_20['CAMEO_INTL_2015'].iloc[0])
Out[40]:
str
In [41]:
df_less_20['CAMEO_INTL_2015'].info()

<class 'pandas.core.series.Series'>
Index: 797077 entries, 1 to 891220
Series name: CAMEO_INTL_2015
Non-Null Count   Dtype 
--------------   ----- 
797077 non-null  object
dtypes: object(1)
memory usage: 12.2+ MB
In [42]:
# Investigate "CAMEO_INTL_2015" and engineer two new variables.

wealth_decades = {
    '11':1,'12':1,'13':1,'14':1,'15':1,
    '21':2,'22':2,'23':2,'24':2,'25':2,
    '31':3,'32':3,'33':3,'34':3,'35':3,
    '41':4,'42':4,'43':4,'44':4,'45':4,
    '51':5,'52':5,'53':5,'54':5,'55':5
}
life_stage_typology_movement = {
    '11':1,'12':2,'13':3,'14':4,'15':5,
    '21':1,'22':2,'23':3,'24':4,'25':5,
    '31':1,'32':2,'33':4,'34':4,'35':5,
    '41':1,'42':2,'43':3,'44':4,'45':5,
    '51':1,'52':2,'53':3,'54':4,'55':5
}
df_less_20['WEALTH'] = df_less_20['CAMEO_INTL_2015']
df_less_20['LIFE_STAGE_TYPOLOGY'] = df_less_20['CAMEO_INTL_2015']
df_less_20['WEALTH'] = df_less_20['WEALTH'].map(wealth_decades)
df_less_20['LIFE_STAGE_TYPOLOGY'] = df_less_20['LIFE_STAGE_TYPOLOGY'].map(life_stage_typology_movement)
df_less_20.drop(columns='CAMEO_INTL_2015', axis=1, inplace=True)

Discussion 1.2.2: Engineer Mixed-Type Features¶

Manually encoded these two features ['CAMEO_INTL_2015', 'LIFE_STAGE_TYPOLOGY'] and droped all other mixed features

Step 1.2.3: Complete Feature Selection¶

In order to finish this step up, you need to make sure that your data frame now only has the columns that you want to keep. To summarize, the dataframe should consist of the following:

  • All numeric, interval, and ordinal type columns from the original dataset.
  • Binary categorical features (all numerically-encoded).
  • Engineered features from other multi-level categorical features and mixed features.

Make sure that for any new columns that you have engineered, that you've excluded the original columns from the final dataset. Otherwise, their values will interfere with the analysis later on the project. For example, you should not keep "PRAEGENDE_JUGENDJAHRE", since its values won't be useful for the algorithm: only the values derived from it in the engineered features you created should be retained. As a reminder, your data should only be from the subset with few or no missing values.

In [43]:
# If there are other re-engineering tasks you need to perform, make sure you
# take care of them here. (Dealing with missing data will come in step 2.1.)
df_less_20.isnull().sum().sum()
Out[43]:
0
In [44]:
mixed_cols = azdias_feature_summary[azdias_feature_summary.type == 'mixed'].attribute
In [45]:
# Do whatever you need to in order to ensure that the dataframe only contains
# the columns that should be passed to the algorithm functions.
for col in mixed_cols:
    if col in df_less_20.columns:
        df_less_20.drop(columns=col, axis=1, inplace=True)
In [46]:
df_less_20.shape
Out[46]:
(797077, 77)

Step 1.3: Create a Cleaning Function¶

Even though you've finished cleaning up the general population demographics data, it's important to look ahead to the future and realize that you'll need to perform the same cleaning steps on the customer demographics data. In this substep, complete the function below to execute the main feature selection, encoding, and re-engineering steps you performed above. Then, when it comes to looking at the customer data in Step 3, you can just run this function on that DataFrame to get the trimmed dataset in a single step.

In [47]:
def binary_vs_multiclass_features(df, categories_cols):
    binary_classes = []
    multi_class = []
    for column in categories_cols:
        if df[column].nunique() == 2:
            binary_classes.append(column)
        else:
            multi_class.append(column)
    return binary_classes, multi_class

def clean_data(df, df_feat_info):
    """
    Perform feature trimming, re-encoding, and engineering for demographics
    data
    
    INPUT: Demographics DataFrame
    OUTPUT: Trimmed and cleaned demographics DataFrame
    """
    
    # Put in code here to execute all main cleaning steps:
    # convert missing value codes into NaNs, ...
    missing_values = df_feat_info['missing_or_unknown'].apply(convert_str_to_list)
    for attribute, missing_vals in zip(df_feat_info['attribute'], missing_values):
        if len(missing_vals)>0:
            for missing_value in missing_vals:
                df.loc[df[attribute] == missing_value, attribute] = np.nan
            
    # remove selected columns and rows, ...
#     missing_values_cols = (df.isnull().sum() / df.shape[0])*100
#     droping_columns = missing_values_cols[missing_values_cols>20].index
    droping_columns=['ALTER_HH', 'GEBURTSJAHR', 'KBA05_BAUMAX', 'KK_KUNDENTYP', 'AGER_TYP','TITEL_KZ']
    df.drop(columns=droping_columns, axis=1, inplace=True)
    
    missing_rows_values = df.isnull().sum(axis=1)
    missing_values_below_threshold = df[missing_rows_values<25]
    df_new = df.loc[missing_values_below_threshold.index]
    df_new = df_new.fillna(df_new.mode().iloc[0])
    
    # select, re-encode, and engineer column values.
    df_feat_info = df_feat_info[df_feat_info.attribute.isin(df_new.columns)]
    
    categorical_data = df_feat_info[df_feat_info['type'] == "categorical"].attribute
    binary, multi_class = binary_vs_multiclass_features(df, categorical_data)
    
    OST_WEST_KZ_mappibg = {'W':0, 'O':1}
    VERS_TYP_ANREDE_KZ_mapping = {1:1, 2:0}
    int_mapping = {0.0:0, 1.0:1}
    
    df_new['VERS_TYP'] = df_new['VERS_TYP'].map(VERS_TYP_ANREDE_KZ_mapping)
    df_new['ANREDE_KZ'] = df_new['ANREDE_KZ'].map(VERS_TYP_ANREDE_KZ_mapping)
    df_new['SOHO_KZ'] = df_new['SOHO_KZ'].map(int_mapping)
    df_new['GREEN_AVANTGARDE'] = df_new['GREEN_AVANTGARDE'].map(int_mapping)
    df_new['OST_WEST_KZ'] = df_new['OST_WEST_KZ'].map(OST_WEST_KZ_mappibg)
#     df_new = pd.get_dummies(data=df_new, columns=multi_class, drop_first=True)
    
    mapping_decades = {
    1:1,2:1,3:2,4:2,5:3,
    6:3,7:3,8:4,9:4,10:5,
    11:5,12:5,13:5,14:6,15:6
    }
    mapping_movement={
        1:1,2:0,3:1,4:0,5:1,
        6:0,7:0,8:1,9:0,10:1,
        11:0,12:1,13:0,14:1,15:0 
    }
    df_new['DECADE'] = df_new['PRAEGENDE_JUGENDJAHRE']
    df_new['MOVEMENT'] = df_new['PRAEGENDE_JUGENDJAHRE']
    df_new['DECADE'] = df_new['DECADE'].map(mapping_decades)
    df_new['MOVEMENT'] = df_new['MOVEMENT'].map(mapping_movement)
    df_new.drop(columns='PRAEGENDE_JUGENDJAHRE', axis=1, inplace=True)

    wealth_decades = {
    '11':1,'12':1,'13':1,'14':1,'15':1,
    '21':2,'22':2,'23':2,'24':2,'25':2,
    '31':3,'32':3,'33':3,'34':3,'35':3,
    '41':4,'42':4,'43':4,'44':4,'45':4,
    '51':5,'52':5,'53':5,'54':5,'55':5
    }
    
    life_stage_typology_movement = {
        '11':1,'12':2,'13':3,'14':4,'15':5,
        '21':1,'22':2,'23':3,'24':4,'25':5,
        '31':1,'32':2,'33':4,'34':4,'35':5,
        '41':1,'42':2,'43':3,'44':4,'45':5,
        '51':1,'52':2,'53':3,'54':4,'55':5
    }
    df_new['WEALTH'] = df_new['CAMEO_INTL_2015']
    df_new['LIFE_STAGE_TYPOLOGY'] = df_new['CAMEO_INTL_2015']
    df_new['WEALTH'] = df_new['WEALTH'].map(wealth_decades)
    df_new['LIFE_STAGE_TYPOLOGY'] = df_new['LIFE_STAGE_TYPOLOGY'].map(life_stage_typology_movement)
    df_new.drop(columns='CAMEO_INTL_2015', axis=1, inplace=True)
    
    mixed_cols = df_feat_info[df_feat_info.type == 'mixed'].attribute
    for col in mixed_cols:
        if col in df_new.columns:
            df_new.drop(columns=col, axis=1, inplace=True)
    
    # Return the cleaned dataframe.
    return df_new
In [48]:
test_df = pd.read_csv("Udacity_AZDIAS_Subset.csv",sep=';')
azdias_feature_summary = pd.read_csv("AZDIAS_Feature_Summary.csv", sep=';')
test_df = clean_data(test_df, azdias_feature_summary)
In [49]:
test_df.shape
Out[49]:
(797906, 77)

Step 2: Feature Transformation¶

Step 2.1: Apply Feature Scaling¶

Before we apply dimensionality reduction techniques to the data, we need to perform feature scaling so that the principal component vectors are not influenced by the natural differences in scale for features. Starting from this part of the project, you'll want to keep an eye on the API reference page for sklearn to help you navigate to all of the classes and functions that you'll need. In this substep, you'll need to check the following:

  • sklearn requires that data not have missing values in order for its estimators to work properly. So, before applying the scaler to your data, make sure that you've cleaned the DataFrame of the remaining missing values. This can be as simple as just removing all data points with missing data, or applying an SimpleImputer to replace all missing values. You might also try a more complicated procedure where you temporarily remove missing values in order to compute the scaling parameters before re-introducing those missing values and applying imputation. Think about how much missing data you have and what possible effects each approach might have on your analysis, and justify your decision in the discussion section below.
  • For the actual scaling function, a StandardScaler instance is suggested, scaling each feature to mean 0 and standard deviation 1.
  • For these classes, you can make use of the .fit_transform() method to both fit a procedure to the data as well as apply the transformation to the data at the same time. Don't forget to keep the fit sklearn objects handy, since you'll be applying them to the customer demographics data towards the end of the project.
In [50]:
# If you've not yet cleaned the dataset of all NaN values, then investigate and
# do that now.
df_less_20.isnull().sum().sum()
Out[50]:
0
In [51]:
df_less_20 = df_less_20.select_dtypes(["int", "float"])
In [52]:
print(df_less_20.isna().sum().sum(), np.isinf(df_less_20).sum().sum())

0 0
In [53]:
sc = StandardScaler()
features_sc = sc.fit_transform(df_less_20) #This likely exceeded your system's available RAM, causing the kernel to crash or disconnect.
In [54]:
# # Apply feature scaling to the general population demographics data.
# Another way to avoid High memory Consumption

# sc = StandardScaler()
# chunk_size = 10000
# for start in range(0, df_less_25.shape[0], chunk_size):
#     chunk = df_less_25[start:start + chunk_size].astype(np.float32)
#     sc.partial_fit(chunk)
# features_sc = np.vstack([sc.transform(df_less_25[start:start + chunk_size]) 
#                          for start in range(0, df_less_25.shape[0], chunk_size)])
In [55]:
df_sc = pd.DataFrame(data=features_sc, columns=df_less_20.columns.tolist())
df_sc.head()
Out[55]:
ALTERSKATEGORIE_GROB ANREDE_KZ CJT_GESAMTTYP FINANZ_MINIMALIST FINANZ_SPARER FINANZ_VORSORGER FINANZ_ANLEGER FINANZ_UNAUFFAELLIGER FINANZ_HAUSBAUER FINANZTYP GFK_URLAUBERTYP GREEN_AVANTGARDE HEALTH_TYP LP_FAMILIE_FEIN LP_FAMILIE_GROB LP_STATUS_FEIN LP_STATUS_GROB NATIONALITAET_KZ RETOURTYP_BK_S SEMIO_SOZ SEMIO_FAM SEMIO_REL SEMIO_MAT SEMIO_VERT SEMIO_LUST SEMIO_ERL SEMIO_KULT SEMIO_RAT SEMIO_KRIT SEMIO_DOM SEMIO_KAEM SEMIO_PFLICHT SEMIO_TRADV SHOPPER_TYP SOHO_KZ VERS_TYP ZABEOTYP ANZ_PERSONEN ANZ_TITEL HH_EINKOMMEN_SCORE W_KEIT_KIND_HH WOHNDAUER_2008 ANZ_HAUSHALTE_AKTIV ANZ_HH_TITEL GEBAEUDETYP KONSUMNAEHE MIN_GEBAEUDEJAHR OST_WEST_KZ KBA05_ANTG1 KBA05_ANTG2 KBA05_ANTG3 KBA05_ANTG4 KBA05_GBZ BALLRAUM EWDICHTE INNENSTADT GEBAEUDETYP_RASTER KKK MOBI_REGIO ONLINE_AFFINITAET REGIOTYP KBA13_ANZAHL_PKW PLZ8_ANTG1 PLZ8_ANTG2 PLZ8_ANTG3 PLZ8_ANTG4 PLZ8_HHZ PLZ8_GBZ ARBEIT ORTSGR_KLS9 RELAT_AB DECADE MOVEMENT WEALTH LIFE_STAGE_TYPOLOGY
0 -1.766173 -0.958121 0.974626 -1.494463 1.538139 -1.040664 1.466401 0.959974 1.338532 -1.342762 0.698969 -0.530654 1.010291 0.308061 0.400728 -0.785867 -0.960239 -0.346574 -1.684892 0.442419 -0.059134 0.003319 -0.463253 -1.685220 -1.109821 -1.435509 -0.578571 1.274161 -0.311629 1.339150 -0.157497 1.517954 1.288519 1.434171 10.855531 -0.922883 1.151377 0.233692 -0.060579 1.025749 -0.729696 0.566381 0.172669 -0.125151 1.956725 -1.304315 -0.383149 -0.517521 -1.010076 -0.966830 -0.595372 2.728773 -1.658408 0.845291 -0.547379 1.701195 -0.799699 -0.791093 -1.281770 0.167099 -0.881658 0.945513 -0.256690 0.212110 0.403256 0.441346 1.454540 0.573590 -0.171711 -0.127228 0.684698 1.098802 0.530654 1.175857 -1.253272
1 0.200733 -0.958121 -0.329665 -1.494463 0.864753 -1.766903 -0.570914 0.245238 1.338532 -1.342762 0.698969 1.884467 1.010291 -0.717540 -0.784616 -0.501956 -0.298630 -0.346574 -0.310441 -0.072758 -1.626956 -0.520178 -0.463253 -0.142659 -0.158824 0.754224 -0.578571 0.064325 1.393241 1.339150 1.448591 -0.638498 -0.410737 0.443733 -0.092119 1.083561 1.151377 -0.631113 -0.060579 -0.268726 -0.729696 0.566381 0.108697 -0.125151 -0.677237 1.275123 -0.383149 -0.517521 -0.299156 1.443506 0.407234 -0.464432 -0.116246 -0.986587 0.034269 -0.270915 0.283625 -0.791093 0.091335 -0.476230 -1.434366 0.229668 0.785871 0.212110 -0.622305 -0.935871 0.418829 0.573590 -0.171711 -0.127228 -0.789243 1.098802 -1.884467 -0.869590 0.755631
2 1.184186 -0.958121 -0.981810 0.683285 -0.482020 1.138052 -0.570914 -1.184235 -0.792444 1.055589 -1.814447 -0.530654 -0.311793 -0.717540 -0.784616 1.201510 1.024588 -0.346574 -0.997667 0.442419 -1.626956 -1.043675 -1.508398 -0.142659 -0.158824 1.301657 -0.067846 -0.540594 -0.311629 -0.303505 0.377866 -0.099385 0.155682 -0.546705 -0.092119 1.083561 -0.269246 -1.495919 -0.060579 -2.210438 0.957646 0.566381 -0.467054 -0.125151 -0.677237 0.630264 1.139883 -0.517521 1.833604 -0.163385 -0.595372 -0.464432 0.654835 -0.070648 -1.129027 0.715140 0.283625 0.269793 0.777888 -1.119559 0.776469 -0.101161 -0.256690 -0.889352 0.403256 -0.935871 -0.616882 0.573590 -1.173497 -0.997766 -0.052273 -0.267171 0.530654 -1.551406 -0.583638
3 0.200733 1.043709 0.974626 0.683285 0.191366 0.411813 -1.250019 0.245238 -0.792444 0.575919 -0.697373 -0.530654 1.010291 1.590062 1.586072 -0.501956 -0.298630 -0.346574 1.064009 0.957596 -0.059134 0.003319 -0.985825 1.399902 -0.158824 -0.340642 0.953604 -1.145512 -0.879919 -1.398609 -1.228222 -0.099385 -0.977156 0.443733 -0.092119 -0.922883 0.441065 1.963302 -0.060579 0.378511 -1.292143 0.566381 -0.339109 -0.125151 -0.677237 0.630264 -0.383149 -0.517521 -0.299156 2.246951 0.407234 -0.464432 -0.116246 -0.986587 0.615916 -1.749998 1.366950 0.269793 0.091335 1.453757 0.223760 -0.560328 -0.256690 1.313572 0.403256 0.441346 -0.616882 -0.337417 0.830075 0.308041 1.421668 -0.267171 0.530654 0.494041 0.085996
4 -1.766173 -0.958121 -0.981810 -0.042631 -1.155407 1.138052 -0.570914 -0.469499 1.338532 -0.863092 -1.814447 -0.530654 1.010291 -0.717540 -0.784616 -0.218045 -0.298630 -0.346574 -0.310441 -1.103113 -0.059134 1.573809 0.059320 -1.171033 -1.109821 -1.435509 0.442879 1.879080 -0.311629 -0.303505 -0.157497 1.517954 1.288519 -1.537143 -0.092119 -0.922883 0.441065 -0.631113 -0.060579 0.378511 0.957646 0.566381 -0.211165 -0.125151 -0.677237 1.275123 -0.383149 -0.517521 0.411764 0.640060 -0.595372 -0.464432 0.654835 0.845291 -1.129027 1.208168 0.283625 1.330680 0.777888 -1.119559 0.223760 1.906627 -0.256690 0.212110 -0.622305 0.441346 1.454540 1.484596 -1.173497 -0.997766 -0.052273 -1.633144 0.530654 1.175857 0.755631
In [56]:
df_sc.describe()
Out[56]:
ALTERSKATEGORIE_GROB ANREDE_KZ CJT_GESAMTTYP FINANZ_MINIMALIST FINANZ_SPARER FINANZ_VORSORGER FINANZ_ANLEGER FINANZ_UNAUFFAELLIGER FINANZ_HAUSBAUER FINANZTYP GFK_URLAUBERTYP GREEN_AVANTGARDE HEALTH_TYP LP_FAMILIE_FEIN LP_FAMILIE_GROB LP_STATUS_FEIN LP_STATUS_GROB NATIONALITAET_KZ RETOURTYP_BK_S SEMIO_SOZ SEMIO_FAM SEMIO_REL SEMIO_MAT SEMIO_VERT SEMIO_LUST SEMIO_ERL SEMIO_KULT SEMIO_RAT SEMIO_KRIT SEMIO_DOM SEMIO_KAEM SEMIO_PFLICHT SEMIO_TRADV SHOPPER_TYP SOHO_KZ VERS_TYP ZABEOTYP ANZ_PERSONEN ANZ_TITEL HH_EINKOMMEN_SCORE W_KEIT_KIND_HH WOHNDAUER_2008 ANZ_HAUSHALTE_AKTIV ANZ_HH_TITEL GEBAEUDETYP KONSUMNAEHE MIN_GEBAEUDEJAHR OST_WEST_KZ KBA05_ANTG1 KBA05_ANTG2 KBA05_ANTG3 KBA05_ANTG4 KBA05_GBZ BALLRAUM EWDICHTE INNENSTADT GEBAEUDETYP_RASTER KKK MOBI_REGIO ONLINE_AFFINITAET REGIOTYP KBA13_ANZAHL_PKW PLZ8_ANTG1 PLZ8_ANTG2 PLZ8_ANTG3 PLZ8_ANTG4 PLZ8_HHZ PLZ8_GBZ ARBEIT ORTSGR_KLS9 RELAT_AB DECADE MOVEMENT WEALTH LIFE_STAGE_TYPOLOGY
count 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05 7.970770e+05
mean -2.310333e-16 -8.441894e-17 -1.064998e-16 -1.709506e-16 -7.889204e-18 1.460974e-16 7.380195e-17 -1.123387e-16 -6.234700e-17 -3.719960e-17 -3.146767e-17 -4.245462e-17 3.788601e-17 -8.584524e-17 -1.472295e-16 1.442610e-16 -3.652211e-17 1.442788e-16 3.116459e-17 -1.132569e-17 4.991147e-17 4.903564e-17 9.685447e-17 1.402050e-16 1.448516e-16 1.194880e-16 -1.287233e-16 -3.907162e-17 -5.409231e-17 3.420884e-17 -1.632218e-16 6.329192e-18 2.152817e-17 7.099392e-17 -6.692898e-17 -4.431326e-17 4.968861e-17 -5.509071e-18 -3.366060e-17 -2.229480e-16 2.092199e-16 2.795542e-17 -5.038393e-17 -3.904487e-18 2.125182e-17 5.640112e-17 1.836209e-14 6.521742e-17 -5.865646e-17 -1.212352e-18 -4.995605e-17 -5.455585e-18 2.264246e-17 -2.290098e-17 8.772617e-17 -2.068487e-16 8.673667e-17 -9.283409e-17 -1.400623e-16 -1.171658e-16 1.911238e-17 -1.205622e-16 -1.698006e-16 2.958362e-16 -3.354472e-17 1.229468e-16 -1.021050e-16 -2.972046e-17 1.876026e-16 1.284113e-16 -1.087373e-16 1.071327e-16 8.464626e-17 -7.484492e-17 -7.095827e-18
std 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00 1.000001e+00
min -1.766173e+00 -9.581212e-01 -1.633955e+00 -1.494463e+00 -1.155407e+00 -1.766903e+00 -1.250019e+00 -1.184235e+00 -1.502770e+00 -1.342762e+00 -1.814447e+00 -5.306541e-01 -1.633876e+00 -7.175404e-01 -7.846158e-01 -1.069778e+00 -9.602388e-01 -3.465739e-01 -1.684892e+00 -1.618291e+00 -1.626956e+00 -1.567172e+00 -1.508398e+00 -1.685220e+00 -1.585320e+00 -1.982942e+00 -1.600021e+00 -1.750430e+00 -2.016498e+00 -1.946160e+00 -1.763585e+00 -1.716724e+00 -1.543574e+00 -1.537143e+00 -9.211894e-02 -9.228826e-01 -1.689869e+00 -1.495919e+00 -6.057891e-02 -2.210438e+00 -1.854591e+00 -3.599102e+00 -4.670539e-01 -1.251514e-01 -6.772368e-01 -1.304315e+00 -2.515393e+00 -5.175208e-01 -1.010076e+00 -9.668305e-01 -5.953723e-01 -4.644322e-01 -1.658408e+00 -1.444556e+00 -1.710675e+00 -1.749998e+00 -2.966347e+00 -1.851980e+00 -1.281770e+00 -1.762888e+00 -1.987075e+00 -1.800935e+00 -2.341812e+00 -3.092277e+00 -1.647867e+00 -9.358709e-01 -2.688305e+00 -2.159431e+00 -2.175284e+00 -1.868305e+00 -1.526214e+00 -2.316130e+00 -1.884467e+00 -1.551406e+00 -1.253272e+00
25% -7.827197e-01 -9.581212e-01 -9.818101e-01 -7.685472e-01 -1.155407e+00 -1.040664e+00 -1.250019e+00 -1.184235e+00 -7.924445e-01 -8.630920e-01 -9.766414e-01 -5.306541e-01 -3.117925e-01 -7.175404e-01 -7.846158e-01 -1.069778e+00 -9.602388e-01 -3.465739e-01 -9.976668e-01 -1.103113e+00 -1.104349e+00 -5.201781e-01 -9.858253e-01 -1.171033e+00 -1.109821e+00 -8.880756e-01 -5.785710e-01 -5.405937e-01 -8.799186e-01 -8.510570e-01 -6.928596e-01 -6.384978e-01 -9.771555e-01 -5.467051e-01 -9.211894e-02 -9.228826e-01 -2.692462e-01 -6.311134e-01 -6.057891e-02 -9.159636e-01 -7.296961e-01 4.569569e-02 -4.670539e-01 -1.251514e-01 -6.772368e-01 -6.594552e-01 -3.831487e-01 -5.175208e-01 -1.010076e+00 -9.668305e-01 -5.953723e-01 -4.644322e-01 -8.873269e-01 -9.865867e-01 -1.129027e+00 -7.639426e-01 -7.996987e-01 -7.910934e-01 -1.281770e+00 -1.119559e+00 -8.816575e-01 -7.000745e-01 -2.566898e-01 -8.893524e-01 -6.223053e-01 -9.358709e-01 -6.168824e-01 -3.374171e-01 -1.717112e-01 -5.624970e-01 -7.892433e-01 -9.501574e-01 5.306541e-01 -8.695902e-01 -1.253272e+00
50% 2.007334e-01 -9.581212e-01 3.224805e-01 -4.263094e-02 1.913661e-01 4.118132e-01 1.081913e-01 -4.694988e-01 -8.211890e-02 9.624855e-02 1.404325e-01 -5.306541e-01 -3.117925e-01 -7.175404e-01 -7.846158e-01 -2.180454e-01 -2.986299e-01 -3.465739e-01 3.767838e-01 -7.275839e-02 -5.913414e-02 3.318662e-03 5.931969e-02 3.715277e-01 3.166748e-01 -3.406423e-01 -6.784616e-02 6.432461e-02 2.566611e-01 2.440464e-01 -1.574969e-01 -9.938472e-02 1.556819e-01 -5.467051e-01 -9.211894e-02 -9.228826e-01 -2.692462e-01 -6.311134e-01 -6.057891e-02 3.785112e-01 3.951986e-01 5.663811e-01 -2.751370e-01 -1.251514e-01 -6.772368e-01 -1.459572e-02 -3.831487e-01 -5.175208e-01 -2.991561e-01 -1.633851e-01 -5.953723e-01 -4.644322e-01 -1.162459e-01 3.873213e-01 3.426866e-02 2.221125e-01 2.836255e-01 2.697932e-01 9.133543e-02 1.670993e-01 2.237603e-01 -2.209434e-01 -2.566898e-01 2.121098e-01 4.032564e-01 4.413458e-01 -6.168824e-01 -3.374171e-01 -1.717112e-01 -1.272278e-01 -5.227270e-02 4.158154e-01 5.306541e-01 4.940411e-01 8.599641e-02
75% 1.184186e+00 1.043709e+00 9.746258e-01 6.832853e-01 8.647525e-01 1.138052e+00 7.872963e-01 9.599741e-01 6.282067e-01 1.055589e+00 9.782380e-01 -5.306541e-01 1.010291e+00 1.077262e+00 9.933998e-01 1.201510e+00 1.024588e+00 -3.465739e-01 1.064009e+00 9.575964e-01 9.860802e-01 5.268155e-01 5.818922e-01 8.857147e-01 7.921735e-01 7.542242e-01 9.536035e-01 6.692430e-01 8.249510e-01 7.915981e-01 9.132284e-01 9.788414e-01 7.221006e-01 4.437331e-01 -9.211894e-02 1.083561e+00 4.410653e-01 2.336919e-01 -6.057891e-02 1.025749e+00 9.576459e-01 5.663811e-01 4.472452e-02 -1.251514e-01 7.532385e-02 6.302637e-01 -7.854226e-02 -5.175208e-01 1.122684e+00 6.400604e-01 4.072339e-01 -4.644322e-01 6.548351e-01 8.452907e-01 1.197564e+00 7.151400e-01 2.836255e-01 2.697932e-01 7.778883e-01 8.104284e-01 7.764692e-01 4.606776e-01 7.858712e-01 2.121098e-01 4.032564e-01 4.413458e-01 4.188286e-01 5.735896e-01 8.300750e-01 7.433107e-01 6.846979e-01 1.098802e+00 5.306541e-01 1.175857e+00 7.556306e-01
max 1.184186e+00 1.043709e+00 1.626771e+00 1.409202e+00 1.538139e+00 1.138052e+00 1.466401e+00 1.674710e+00 1.338532e+00 1.055589e+00 1.257506e+00 1.884467e+00 1.010291e+00 1.846462e+00 1.586072e+00 1.485421e+00 1.686197e+00 3.946528e+00 1.064009e+00 1.472774e+00 1.508687e+00 1.573809e+00 1.627037e+00 1.399902e+00 1.267672e+00 1.301657e+00 1.464328e+00 1.879080e+00 1.393241e+00 1.339150e+00 1.448591e+00 1.517954e+00 1.854938e+00 1.434171e+00 1.085553e+01 1.083561e+00 1.861688e+00 3.742032e+01 5.830879e+01 1.025749e+00 9.576459e-01 5.663811e-01 3.753249e+01 7.099171e+01 1.956725e+00 2.564842e+00 6.927405e+00 1.932289e+00 1.833604e+00 2.246951e+00 2.412446e+00 2.728773e+00 1.425916e+00 1.303260e+00 1.197564e+00 1.701195e+00 1.366950e+00 1.330680e+00 2.150994e+00 1.453757e+00 1.329178e+00 4.758598e+00 1.828432e+00 1.313572e+00 1.428818e+00 1.818562e+00 1.454540e+00 1.484596e+00 1.831861e+00 1.613849e+00 1.421668e+00 1.098802e+00 5.306541e-01 1.175857e+00 1.425265e+00

Discussion 2.1: Apply Feature Scaling¶

used a StandardScaler instance, scaling each feature to mean 0 and standard 1

In [57]:
df_sc.shape
Out[57]:
(797077, 75)

Step 2.2: Perform Dimensionality Reduction¶

On your scaled data, you are now ready to apply dimensionality reduction techniques.

  • Use sklearn's PCA class to apply principal component analysis on the data, thus finding the vectors of maximal variance in the data. To start, you should not set any parameters (so all components are computed) or set a number of components that is at least half the number of features (so there's enough features to see the general trend in variability).
  • Check out the ratio of variance explained by each principal component as well as the cumulative variance explained. Try plotting the cumulative or sequential values using matplotlib's plot() function. Based on what you find, select a value for the number of transformed features you'll retain for the clustering part of the project.
  • Once you've made a choice for the number of components to keep, make sure you re-fit a PCA instance to perform the decided-on transformation.
In [58]:
# Apply PCA to the data.
pca= PCA()
pca = pca.fit(df_sc)
In [59]:
def scree_plot(pca):
    '''
    Creates a scree plot associated with the principal components 
    
    INPUT: pca - the result of instantian of PCA in scikit learn
            
    OUTPUT:
            None
    '''
    num_components=len(pca.explained_variance_ratio_)
    ind = np.arange(num_components)
    vals = pca.explained_variance_ratio_
 
    plt.figure(figsize=(25, 10))
    ax = plt.subplot(111)
    cumvals = np.cumsum(vals)
    ax.bar(ind, vals)
    ax.plot(ind, cumvals)
    for i in range(num_components):
        ax.annotate(r"%s%%" % ((str(vals[i]*100)[:4])), (ind[i]+0.2, vals[i]), va="bottom", ha="center", fontsize=8)
 
    ax.xaxis.set_tick_params(width=0)
    ax.yaxis.set_tick_params(width=2, length=12)
 
    ax.set_xlabel("Principal Component")
    ax.set_ylabel("Variance-Explained Ratio")
    plt.title('Explained Variance Per Principal Component')
In [60]:
# Investigate the variance accounted for by each principal component.
scree_plot(pca)
No description has been provided for this image
In [61]:
# Re-apply PCA to the data while selecting for number of components to retain.
pca_35 = PCA(n_components=35)
features_pca = pca_35.fit_transform(df_sc)

Discussion 2.2: Perform Dimensionality Reduction¶

Used only 35 Components and this retains more than 80% of data variability

Step 2.3: Interpret Principal Components¶

Now that we have our transformed principal components, it's a nice idea to check out the weight of each variable on the first few components to see if they can be interpreted in some fashion.

As a reminder, each principal component is a unit vector that points in the direction of highest variance (after accounting for the variance captured by earlier principal components). The further a weight is from zero, the more the principal component is in the direction of the corresponding feature. If two features have large weights of the same sign (both positive or both negative), then increases in one tend expect to be associated with increases in the other. To contrast, features with different signs can be expected to show a negative correlation: increases in one variable should result in a decrease in the other.

  • To investigate the features, you should map each weight to their corresponding feature name, then sort the features according to weight. The most interesting features for each principal component, then, will be those at the beginning and end of the sorted list. Use the data dictionary document to help you understand these most prominent features, their relationships, and what a positive or negative value on the principal component might indicate.
  • You should investigate and interpret feature associations from the first three principal components in this substep. To help facilitate this, you should write a function that you can call at any time to print the sorted list of feature weights, for the i-th principal component. This might come in handy in the next step of the project, when you interpret the tendencies of the discovered clusters.
In [62]:
d = pd.DataFrame(pca.components_, columns=pca.feature_names_in_)
In [63]:
# Map weights for the first principal component to corresponding feature names
# and then print the linked values, sorted by weight.
# HINT: Try defining a function here or in a new cell that you can reuse in the
# other cells.
def get_features_weights(pca, principal_component):
    weights = pd.DataFrame(pca.components_, columns=pca.feature_names_in_).iloc[principal_component]
    weights.sort_values(ascending=False, inplace=True)
    return weights
In [64]:
get_features_weights(pca_35, 0)
Out[64]:
PLZ8_ANTG3               0.202463
HH_EINKOMMEN_SCORE       0.200525
PLZ8_ANTG4               0.194955
WEALTH                   0.190943
ORTSGR_KLS9              0.173692
EWDICHTE                 0.172053
FINANZ_HAUSBAUER         0.162667
KBA05_ANTG4              0.142546
ZABEOTYP                 0.138781
PLZ8_ANTG2               0.138755
FINANZ_SPARER            0.136884
KBA05_ANTG3              0.131142
ANZ_HAUSHALTE_AKTIV      0.127069
ARBEIT                   0.126217
RELAT_AB                 0.119590
MOVEMENT                 0.116436
SEMIO_PFLICHT            0.104376
SEMIO_REL                0.098978
DECADE                   0.096911
SEMIO_RAT                0.088039
SEMIO_TRADV              0.078800
GEBAEUDETYP              0.071183
FINANZ_UNAUFFAELLIGER    0.069220
FINANZ_ANLEGER           0.068919
SEMIO_MAT                0.068511
GFK_URLAUBERTYP          0.065041
SEMIO_FAM                0.063852
SEMIO_KULT               0.056960
REGIOTYP                 0.056560
NATIONALITAET_KZ         0.055843
OST_WEST_KZ              0.047274
W_KEIT_KIND_HH           0.046416
CJT_GESAMTTYP            0.045479
SEMIO_KAEM               0.043571
PLZ8_HHZ                 0.039261
KKK                      0.039079
HEALTH_TYP               0.037430
SEMIO_SOZ                0.030222
ANZ_HH_TITEL             0.030154
SEMIO_DOM                0.027872
KBA05_ANTG2              0.017750
SEMIO_KRIT               0.015031
SOHO_KZ                 -0.002251
ANZ_TITEL               -0.005473
SHOPPER_TYP             -0.009552
RETOURTYP_BK_S          -0.016409
ANREDE_KZ               -0.016742
VERS_TYP                -0.031564
MIN_GEBAEUDEJAHR        -0.044985
SEMIO_VERT              -0.047283
ONLINE_AFFINITAET       -0.051242
WOHNDAUER_2008          -0.059100
SEMIO_ERL               -0.061847
KBA13_ANZAHL_PKW        -0.064754
FINANZTYP               -0.065201
SEMIO_LUST              -0.065287
LP_FAMILIE_FEIN         -0.085653
LP_FAMILIE_GROB         -0.087429
ANZ_PERSONEN            -0.088204
FINANZ_VORSORGER        -0.103306
GEBAEUDETYP_RASTER      -0.106071
ALTERSKATEGORIE_GROB    -0.107049
BALLRAUM                -0.110738
LIFE_STAGE_TYPOLOGY     -0.114973
GREEN_AVANTGARDE        -0.116436
INNENSTADT              -0.144319
PLZ8_GBZ                -0.148180
KONSUMNAEHE             -0.150795
KBA05_ANTG1             -0.201369
KBA05_GBZ               -0.202074
PLZ8_ANTG1              -0.202915
MOBI_REGIO              -0.209710
FINANZ_MINIMALIST       -0.216868
LP_STATUS_GROB          -0.227282
LP_STATUS_FEIN          -0.227380
Name: 0, dtype: float64
In [65]:
# Map weights for the second principal component to corresponding feature names
# and then print the linked values, sorted by weight.
get_features_weights(pca_35, 1)
Out[65]:
ALTERSKATEGORIE_GROB     0.255069
FINANZ_VORSORGER         0.228016
SEMIO_ERL                0.222073
SEMIO_LUST               0.177622
RETOURTYP_BK_S           0.160787
W_KEIT_KIND_HH           0.121259
FINANZ_HAUSBAUER         0.110319
SEMIO_KRIT               0.109867
FINANZTYP                0.109242
SHOPPER_TYP              0.104887
SEMIO_KAEM               0.102485
PLZ8_ANTG3               0.083165
EWDICHTE                 0.081655
PLZ8_ANTG4               0.081468
ORTSGR_KLS9              0.080511
FINANZ_MINIMALIST        0.070026
WEALTH                   0.068366
KBA05_ANTG4              0.065781
SEMIO_DOM                0.064192
ARBEIT                   0.062283
RELAT_AB                 0.058467
ANZ_HAUSHALTE_AKTIV      0.058441
PLZ8_ANTG2               0.057089
WOHNDAUER_2008           0.056117
HH_EINKOMMEN_SCORE       0.054124
KBA05_ANTG3              0.043437
ANZ_HH_TITEL             0.029295
OST_WEST_KZ              0.028101
MOVEMENT                 0.017583
GEBAEUDETYP              0.016704
PLZ8_HHZ                 0.012453
REGIOTYP                 0.008201
ANZ_TITEL                0.006120
LIFE_STAGE_TYPOLOGY      0.003883
KBA05_ANTG2             -0.000909
KKK                     -0.001647
SOHO_KZ                 -0.002035
ZABEOTYP                -0.012006
GREEN_AVANTGARDE        -0.017583
VERS_TYP                -0.026473
LP_STATUS_GROB          -0.027215
KBA13_ANZAHL_PKW        -0.033046
GFK_URLAUBERTYP         -0.033734
GEBAEUDETYP_RASTER      -0.040319
MIN_GEBAEUDEJAHR        -0.045702
LP_STATUS_FEIN          -0.046233
BALLRAUM                -0.053428
HEALTH_TYP              -0.057050
SEMIO_VERT              -0.061026
KONSUMNAEHE             -0.063158
KBA05_ANTG1             -0.063605
PLZ8_GBZ                -0.064474
INNENSTADT              -0.066484
NATIONALITAET_KZ        -0.067412
MOBI_REGIO              -0.068692
ANZ_PERSONEN            -0.074214
KBA05_GBZ               -0.079498
ANREDE_KZ               -0.081394
LP_FAMILIE_FEIN         -0.081708
PLZ8_ANTG1              -0.082137
LP_FAMILIE_GROB         -0.083130
SEMIO_SOZ               -0.095915
CJT_GESAMTTYP           -0.124383
SEMIO_MAT               -0.156507
ONLINE_AFFINITAET       -0.168867
SEMIO_RAT               -0.170135
SEMIO_FAM               -0.176008
FINANZ_ANLEGER          -0.202591
SEMIO_KULT              -0.210367
SEMIO_PFLICHT           -0.225214
SEMIO_TRADV             -0.225275
FINANZ_UNAUFFAELLIGER   -0.226493
FINANZ_SPARER           -0.234015
DECADE                  -0.239448
SEMIO_REL               -0.249845
Name: 1, dtype: float64
In [66]:
# Map weights for the third principal component to corresponding feature names
# and then print the linked values, sorted by weight.
get_features_weights(pca_35, 2)
Out[66]:
ANREDE_KZ                0.362304
SEMIO_VERT               0.335494
SEMIO_SOZ                0.260058
SEMIO_FAM                0.254202
SEMIO_KULT               0.240607
FINANZ_MINIMALIST        0.142352
FINANZTYP                0.114726
RETOURTYP_BK_S           0.102427
W_KEIT_KIND_HH           0.087106
FINANZ_VORSORGER         0.086220
SEMIO_REL                0.082626
SEMIO_MAT                0.064778
ALTERSKATEGORIE_GROB     0.062423
PLZ8_ANTG4               0.055788
ORTSGR_KLS9              0.055372
PLZ8_ANTG3               0.055002
EWDICHTE                 0.053721
SEMIO_LUST               0.051449
ARBEIT                   0.043548
GREEN_AVANTGARDE         0.039979
RELAT_AB                 0.037806
WEALTH                   0.037426
PLZ8_ANTG2               0.036156
KBA05_ANTG4              0.033985
ANZ_HAUSHALTE_AKTIV      0.030513
LP_STATUS_GROB           0.026583
WOHNDAUER_2008           0.026022
OST_WEST_KZ              0.023063
KBA05_ANTG3              0.015635
ANZ_HH_TITEL             0.014149
GEBAEUDETYP              0.013053
LP_STATUS_FEIN           0.011432
ANZ_TITEL                0.008851
PLZ8_HHZ                 0.005494
SOHO_KZ                  0.000119
REGIOTYP                -0.005266
KBA05_ANTG2             -0.005640
VERS_TYP                -0.005703
HH_EINKOMMEN_SCORE      -0.008499
KKK                     -0.013235
LIFE_STAGE_TYPOLOGY     -0.016807
GFK_URLAUBERTYP         -0.017855
NATIONALITAET_KZ        -0.019092
MIN_GEBAEUDEJAHR        -0.019238
ANZ_PERSONEN            -0.020298
HEALTH_TYP              -0.026378
KBA13_ANZAHL_PKW        -0.027700
KBA05_ANTG1             -0.028520
LP_FAMILIE_FEIN         -0.031130
MOBI_REGIO              -0.032856
LP_FAMILIE_GROB         -0.033014
KBA05_GBZ               -0.034014
CJT_GESAMTTYP           -0.036091
GEBAEUDETYP_RASTER      -0.036175
FINANZ_HAUSBAUER        -0.037454
MOVEMENT                -0.039979
BALLRAUM                -0.040034
KONSUMNAEHE             -0.044949
PLZ8_GBZ                -0.046925
INNENSTADT              -0.050255
ZABEOTYP                -0.054568
ONLINE_AFFINITAET       -0.056298
PLZ8_ANTG1              -0.057025
SEMIO_PFLICHT           -0.063915
SEMIO_TRADV             -0.064992
FINANZ_SPARER           -0.092006
FINANZ_UNAUFFAELLIGER   -0.093732
DECADE                  -0.097740
SHOPPER_TYP             -0.123121
FINANZ_ANLEGER          -0.176582
SEMIO_ERL               -0.184397
SEMIO_RAT               -0.201295
SEMIO_KRIT              -0.271099
SEMIO_DOM               -0.307329
SEMIO_KAEM              -0.330173
Name: 2, dtype: float64

Discussion 2.3: Interpret Principal Components¶

  • First principal component

    • positive values:

      1. PLZ8_ANTG3(Number of 6-10 family houses in the PLZ8 region)
      2. HH_EINKOMMEN_SCORE (Estimated household net income)
      3. WEALTH(Households wealth)
      4. PLZ8_ANTG4 ( Number of 10+ family houses in the PLZ8 region)

    • negative values:

      1. FINANZ_MINIMALIST(Financial typology:low financial interest)
      2. MOBI_REGIO(Movement patterns)
      3. PLZ8_ANTG1(Number of 1-2 family houses in the PLZ8 region)
      4. KBA05_GBZ(Number of buildings in the microcell)
      5. LP_STATUS_GROB(Social status, rough scale)
      6. LP_STATUS_FEIN(Social status, fine scale)

      based on the observations, the first component has a correlation with a number of buildings, family (family members), social status, household net income, and wealth.

  • Second principal component

    • positive values:

      1. ALTERSKATEGORIE_GROB(Estimated age based on given name analysis)
      2. FINANZ_VORSORGER(Financial typology:be prepared)
      3. SEMIO_LUST(Personality typology)
      4. SEMIO_ERL(Personality typology:event-oriented)

    • negative values:

      1. DECADE(generation)
      2. FINANZ_SPARER(Financial typology:money-saver)
      3. FINANZ_UNAUFFAELLIGER(Financial typology:inconspicuous)
      4. SEMIO_REL(Personality typology:religious)
      5. SEMIO_TRADV(Personality typology)

      based on the observations, second component is related to Financial typology, Personality typology, age, energy consumption, and decade.

  • Third principal component

    • positive values:

      1. ANREDE_KZ(Gender:femal,male)
      2. SEMIO_VERT(Personality typology:dreamful)
      3. SEMIO_FAM (Personality typology:family-minded)
      4. SEMIO_SOZ (Personality typology:socially-minded)

    • negative values:

      1. SEMIO_KAEM (Personality typology:combative attitude)
      2. SEMIO_DOM (Personality typology:dominant-minded)
      3. SEMIO_KRIT (Personality typology:critical-minded)
      4. SEMIO_ERL (Personality typology:event-oriented)

      based on the observations, third component has a strong relationship with Personality typology and gender.

Step 3: Clustering¶

Step 3.1: Apply Clustering to General Population¶

You've assessed and cleaned the demographics data, then scaled and transformed them. Now, it's time to see how the data clusters in the principal components space. In this substep, you will apply k-means clustering to the dataset and use the average within-cluster distances from each point to their assigned cluster's centroid to decide on a number of clusters to keep.

  • Use sklearn's KMeans class to perform k-means clustering on the PCA-transformed data.
  • Then, compute the average difference from each point to its assigned cluster's center. Hint: The KMeans object's .score() method might be useful here, but note that in sklearn, scores tend to be defined so that larger is better. Try applying it to a small, toy dataset, or use an internet search to help your understanding.
  • Perform the above two steps for a number of different cluster counts. You can then see how the average distance decreases with an increasing number of clusters. However, each additional cluster provides a smaller net benefit. Use this fact to select a final number of clusters in which to group the data. Warning: because of the large size of the dataset, it can take a long time for the algorithm to resolve. The more clusters to fit, the longer the algorithm will take. You should test for cluster counts through at least 10 clusters to get the full picture, but you shouldn't need to test for a number of clusters above about 30.
  • Once you've selected a final number of clusters to use, re-fit a KMeans instance to perform the clustering operation. Make sure that you also obtain the cluster assignments for the general demographics data, since you'll be using them in the final Step 3.3.
In [67]:
def kmenas_run(df, n_clusters):
    kmeans = KMeans(n_clusters=n_clusters)
    kmean_model = kmeans.fit(df)
    score = np.abs(kmean_model.score(df))
    return score
In [68]:
# Over a number of different cluster counts...
centroides = list(range(10, 25))
result_scores = []
for centroide in centroides:
    # run k-means clustering on the data and...
    # compute the average within-cluster distances.
    result_scores.append(kmenas_run(features_pca,centroide))
In [69]:
# Investigate the change in within-cluster distance across number of clusters.
# HINT: Use matplotlib's plot function to visualize this relationship.
def plot_Kmeans_score(scores, centroides):
    plt.figure(figsize=(10, 6))
    plt.plot(centroides,scores, marker='d',linestyle='--', color='g')
    plt.xlabel('number of clusters (K)')
    plt.ylabel('Average distance form centroide')
    plt.title("Relationship between K and Avg distance from centroide")
    plt.show()
    

plot_Kmeans_score(result_scores, centroides)
No description has been provided for this image
In [70]:
# Re-fit the k-means model with the selected number of clusters and obtain
# cluster predictions for the general population demographics data.

kmeans = KMeans(n_clusters=22)
kmean_model = kmeans.fit(features_pca)
preds=kmean_model.predict(features_pca)

Discussion 3.1: Apply Clustering to General Population¶

Based on the observation we saw in the plot we can't see the elbow but we can see the score decreases with the number of clusters increase between 18-22 clusters so it seems that 22 is a good number of clusters to proceed with .

Step 3.2: Apply All Steps to the Customer Data¶

Now that you have clusters and cluster centers for the general population, it's time to see how the customer data maps on to those clusters. Take care to not confuse this for re-fitting all of the models to the customer data. Instead, you're going to use the fits from the general population to clean, transform, and cluster the customer data. In the last step of the project, you will interpret how the general population fits apply to the customer data.

  • Don't forget when loading in the customers data, that it is semicolon (;) delimited.
  • Apply the same feature wrangling, selection, and engineering steps to the customer demographics using the clean_data() function you created earlier. (You can assume that the customer demographics data has similar meaning behind missing data patterns as the general demographics data.)
  • Use the sklearn objects from the general demographics data, and apply their transformations to the customers data. That is, you should not be using a .fit() or .fit_transform() method to re-fit the old objects, nor should you be creating new sklearn objects! Carry the data through the feature scaling, PCA, and clustering steps, obtaining cluster assignments for all of the data in the customer demographics data.
In [71]:
# Load in the customer demographics data.
customers = pd.read_csv('Udacity_CUSTOMERS_Subset.csv',sep=';')
feat_info = pd.read_csv("AZDIAS_Feature_Summary.csv", sep=';')
In [72]:
customers.head()
Out[72]:
AGER_TYP ALTERSKATEGORIE_GROB ANREDE_KZ CJT_GESAMTTYP FINANZ_MINIMALIST FINANZ_SPARER FINANZ_VORSORGER FINANZ_ANLEGER FINANZ_UNAUFFAELLIGER FINANZ_HAUSBAUER FINANZTYP GEBURTSJAHR GFK_URLAUBERTYP GREEN_AVANTGARDE HEALTH_TYP LP_LEBENSPHASE_FEIN LP_LEBENSPHASE_GROB LP_FAMILIE_FEIN LP_FAMILIE_GROB LP_STATUS_FEIN LP_STATUS_GROB NATIONALITAET_KZ PRAEGENDE_JUGENDJAHRE RETOURTYP_BK_S SEMIO_SOZ SEMIO_FAM SEMIO_REL SEMIO_MAT SEMIO_VERT SEMIO_LUST SEMIO_ERL SEMIO_KULT SEMIO_RAT SEMIO_KRIT SEMIO_DOM SEMIO_KAEM SEMIO_PFLICHT SEMIO_TRADV SHOPPER_TYP SOHO_KZ TITEL_KZ VERS_TYP ZABEOTYP ALTER_HH ANZ_PERSONEN ANZ_TITEL HH_EINKOMMEN_SCORE KK_KUNDENTYP W_KEIT_KIND_HH WOHNDAUER_2008 ANZ_HAUSHALTE_AKTIV ANZ_HH_TITEL GEBAEUDETYP KONSUMNAEHE MIN_GEBAEUDEJAHR OST_WEST_KZ WOHNLAGE CAMEO_DEUG_2015 CAMEO_DEU_2015 CAMEO_INTL_2015 KBA05_ANTG1 KBA05_ANTG2 KBA05_ANTG3 KBA05_ANTG4 KBA05_BAUMAX KBA05_GBZ BALLRAUM EWDICHTE INNENSTADT GEBAEUDETYP_RASTER KKK MOBI_REGIO ONLINE_AFFINITAET REGIOTYP KBA13_ANZAHL_PKW PLZ8_ANTG1 PLZ8_ANTG2 PLZ8_ANTG3 PLZ8_ANTG4 PLZ8_BAUMAX PLZ8_HHZ PLZ8_GBZ ARBEIT ORTSGR_KLS9 RELAT_AB
0 2 4 1 5.0 5 1 5 1 2 2 2 0 4.0 1 1 20.0 5.0 2.0 2.0 10.0 5.0 1 4 5.0 6 5 2 6 6 7 3 4 1 3 1 1 2 1 3 0.0 0.0 1 3 10.0 2.0 0.0 1.0 NaN 6.0 9.0 1.0 0.0 1.0 5.0 1992.0 W 7.0 1 1A 13 2.0 2.0 0.0 0.0 0.0 4.0 3.0 2.0 4.0 4.0 1.0 4.0 3.0 1.0 1201.0 3.0 3.0 1.0 0.0 1.0 5.0 5.0 1.0 2.0 1.0
1 -1 4 1 NaN 5 1 5 1 3 2 2 0 NaN 0 1 NaN NaN NaN NaN NaN NaN 1 0 NaN 3 6 2 6 7 5 3 4 1 3 3 2 4 1 3 0.0 0.0 1 3 11.0 3.0 0.0 NaN NaN 0.0 9.0 NaN NaN NaN 5.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 -1 4 2 2.0 5 1 5 1 4 4 2 0 3.0 1 2 13.0 3.0 1.0 1.0 10.0 5.0 1 4 5.0 2 2 1 3 3 7 7 1 2 7 5 6 4 1 1 0.0 0.0 2 3 6.0 1.0 0.0 1.0 NaN 6.0 9.0 1.0 0.0 8.0 1.0 1992.0 W 2.0 5 5D 34 2.0 2.0 0.0 0.0 0.0 3.0 7.0 4.0 1.0 3.0 3.0 3.0 1.0 7.0 433.0 2.0 3.0 3.0 1.0 3.0 3.0 2.0 3.0 5.0 3.0
3 1 4 1 2.0 5 1 5 2 1 2 6 0 10.0 0 2 0.0 0.0 0.0 0.0 9.0 4.0 1 1 3.0 6 5 3 4 7 5 3 4 3 3 3 3 3 4 0 0.0 0.0 1 1 8.0 0.0 0.0 4.0 NaN NaN 9.0 0.0 NaN 2.0 2.0 1992.0 W 7.0 4 4C 24 3.0 0.0 0.0 0.0 1.0 4.0 7.0 1.0 7.0 4.0 3.0 4.0 2.0 6.0 755.0 3.0 2.0 1.0 0.0 1.0 3.0 4.0 1.0 3.0 1.0
4 -1 3 1 6.0 3 1 4 4 5 2 2 1960 2.0 0 3 31.0 10.0 10.0 5.0 1.0 1.0 1 8 5.0 4 5 4 6 5 6 4 5 5 3 5 2 5 4 1 0.0 0.0 2 1 20.0 4.0 0.0 6.0 2.0 2.0 9.0 7.0 0.0 3.0 1.0 1992.0 W 3.0 7 7B 41 0.0 3.0 2.0 0.0 0.0 3.0 3.0 4.0 4.0 3.0 4.0 3.0 5.0 7.0 513.0 2.0 4.0 2.0 1.0 2.0 3.0 3.0 3.0 5.0 1.0
In [73]:
# Apply preprocessing, feature transformation, and clustering from the general
# demographics onto the customer data, obtaining cluster predictions for the
# customer demographics data.
clean_customers = clean_data(customers, feat_info)
In [74]:
def add_missing_dummy_columns(df, columns):
    missing_cols = set(columns) - set(df.columns)
    print(missing_cols)
    for c in missing_cols:
        df[c] = 0
           
def fix_columns(d, columns):  
    add_missing_dummy_columns(df, columns)
    # make sure we have all the columns we need
    assert(set(columns) - set(df.columns) == set())
    extra_cols = set(df.columns) - set(columns)
    if extra_cols:
        print("extra columns:", extra_cols)
    df = df[columns]
    return df
In [75]:
clean_customers = clean_customers.select_dtypes(['float', 'int'])
In [76]:
clean_customers.isnull().sum().sum()
Out[76]:
0
In [77]:
customer_sc = sc.transform(clean_customers)
In [78]:
customer_sc = pd.DataFrame(data=customer_sc, columns=clean_customers.columns.tolist())
customer_sc.shape
Out[78]:
(141701, 75)
In [79]:
customers_pca=pca_35.transform(customer_sc)
pred_customers= kmean_model.predict(customers_pca)

Step 3.3: Compare Customer Data to Demographics Data¶

At this point, you have clustered data based on demographics of the general population of Germany, and seen how the customer data for a mail-order sales company maps onto those demographic clusters. In this final substep, you will compare the two cluster distributions to see where the strongest customer base for the company is.

Consider the proportion of persons in each cluster for the general population, and the proportions for the customers. If we think the company's customer base to be universal, then the cluster assignment proportions should be fairly similar between the two. If there are only particular segments of the population that are interested in the company's products, then we should see a mismatch from one to the other. If there is a higher proportion of persons in a cluster for the customer data compared to the general population (e.g. 5% of persons are assigned to a cluster for the general population, but 15% of the customer data is closest to that cluster's centroid) then that suggests the people in that cluster to be a target audience for the company. On the other hand, the proportion of the data in a cluster being larger in the general population than the customer data (e.g. only 2% of customers closest to a population centroid that captures 6% of the data) suggests that group of persons to be outside of the target demographics.

Take a look at the following points in this step:

  • Compute the proportion of data points in each cluster for the general population and the customer data. Visualizations will be useful here: both for the individual dataset proportions, but also to visualize the ratios in cluster representation between groups. Seaborn's countplot() or barplot() function could be handy.
    • Recall the analysis you performed in step 1.1.3 of the project, where you separated out certain data points from the dataset if they had more than a specified threshold of missing values. If you found that this group was qualitatively different from the main bulk of the data, you should treat this as an additional data cluster in this analysis. Make sure that you account for the number of data points in this subset, for both the general population and customer datasets, when making your computations!
  • Which cluster or clusters are overrepresented in the customer dataset compared to the general population? Select at least one such cluster and infer what kind of people might be represented by that cluster. Use the principal component interpretations from step 2.3 or look at additional components to help you make this inference. Alternatively, you can use the .inverse_transform() method of the PCA and StandardScaler objects to transform centroids back to the original data space and interpret the retrieved values directly.
  • Perform a similar investigation for the underrepresented clusters. Which cluster or clusters are underrepresented in the customer dataset compared to the general population, and what kinds of people are typified by these clusters?
In [107]:
# Compare the proportion of data in each cluster for the customer data to the
# proportion of data in each cluster for the general population.
def difference_plot(general, customers, pca_customers, feature_names, pca, scaler, centroids):
    """
    Parameters:
    - general: Array of cluster labels for the general population.
    - customers: Array of cluster labels for the customer dataset.
    - pca_customers: PCA-transformed customer data.
    - preds_customers: Predicted cluster labels for customers.
    - feature_names: List of original feature names (for interpretation).
    - pca: Fitted PCA object.
    - scaler: Fitted StandardScaler object.
    - centroids: Cluster centroids in PCA space.
    - azdias_columns: Column names of the original dataset (e.g., azdias_below20.columns).
    
    Returns:
    - overrepresented_clusters: List of clusters where customer proportion > general proportion.
    - underrepresented_clusters: List of clusters where customer proportion < general proportion.
    - cluster_data: Dictionary of DataFrames with original data per cluster.
    - centroid_df: DataFrame of centroid values in original space.
    """
    # Input validation
    if len(general) == 0 or len(customers) == 0:
        raise ValueError("Input arrays cannot be empty.")
    
    # Get unique clusters and counts
    all_clusters = np.unique(np.concatenate([general, customers]))
    general_counts = pd.Series(general).value_counts().reindex(all_clusters, fill_value=0)
    customers_counts = pd.Series(customers).value_counts().reindex(all_clusters, fill_value=0)
    # Calculate proportions
    general_proportions = general_counts / len(general) * 100
    customers_proportions = customers_counts / len(customers) * 100
    # Identify overrepresented clusters
    overrepresentation_ratio = customers_proportions / general_proportions
    overrepresented_clusters = overrepresentation_ratio[overrepresentation_ratio > 1].index.tolist()
    underrepresented_clusters = overrepresentation_ratio[overrepresentation_ratio < 1].index.tolist()
    # Create DataFrame for plotting
    plot_data = pd.DataFrame({
        'Cluster': all_clusters,
        'General': general_proportions,
        'Customers': customers_proportions
    }).melt(id_vars='Cluster', value_vars=['General', 'Customers'], var_name='Dataset', value_name='Percentage')
    
    # Plot grouped bar chart
    plt.figure(figsize=(10, 6))
    sns.barplot(x='Cluster', y='Percentage', hue='Dataset', data=plot_data, palette=['skyblue', 'salmon'])
    plt.title('Cluster Proportions: General Population vs. Customers', fontsize=12)
    plt.xlabel('Cluster', fontsize=10)
    plt.ylabel('Percentage (%)', fontsize=10)
    plt.xticks(rotation=45, ha='right')
    plt.grid(True, axis='y', linestyle='--', alpha=0.7)
    plt.legend(title='Dataset')
    plt.tight_layout()
    plt.show()
    
    # Print overrepresentation
    print("\nOverrepresented clusters (ratio > 1):")
    if overrepresented_clusters:
        for cluster in overrepresented_clusters:
            ratio = overrepresentation_ratio[cluster]
            print(f"Cluster {cluster}: Customer Proportion = {customers_proportions[cluster]:.2f}%, "
                  f"General Proportion = {general_proportions[cluster]:.2f}%, Ratio = {ratio:.2f}")
    else:
        print("None")
    
    # Print underrepresented clusters
    print("\nUnderrepresented clusters (ratio < 1):")
    if underrepresented_clusters:
        for cluster in underrepresented_clusters:
            ratio = overrepresentation_ratio[cluster]
            print(f"Cluster {cluster}: Customer Proportion = {customers_proportions[cluster]:.2f}%, "
                  f"General Proportion = {general_proportions[cluster]:.2f}%, Ratio = {ratio:.2f}")
    else:
        print("None")  
    
    ########################################################################################
    # Extracting the Clusters Data
    cluster_data = {}
    # Interpret overrepresented clusters (if PCA and scaler are provided)
    if (overrepresented_clusters or underrepresented_clusters) and feature_names is not None and pca is not None and scaler is not None and centroids is not None:        
        for cluster in all_clusters:
            mask = (cluster == customers) 
            extracted_data = scaler.inverse_transform(np.dot(pca_customers[mask], pca.components_))
            cluster_data[cluster] = pd.DataFrame(extracted_data, columns=feature_names)
            
        centroid_std = pca.inverse_transform(centroids)
        centroid_orig = scaler.inverse_transform(centroid_std)
        centroid_df = pd.DataFrame(centroid_orig, columns=feature_names)
        loadings = pd.DataFrame(pca.components_.T, columns=[f'PC{i+1}' for i in range(pca.n_components_)], 
                                   index=feature_names)
        
    # Prominent features from PCA loadings (e.g., top 3 from Component 1)
    loadings = pd.DataFrame(pca.components_.T, columns=[f'PC{i+1}' for i in range(pca.n_components_)], index=feature_names)
    prominent_features = loadings.abs().nlargest(4, columns=['PC1']).index.tolist()  # Adjust based on your plot
    
    # Create distribution plots for prominent features in overrepresented clusters
    if overrepresented_clusters:
        print("\nDistribution Plots for Prominent Features in Overrepresented Clusters:")
        print(prominent_features, "\n")
        for cluster in overrepresented_clusters:
            # Create figure with subplots (number of subplots matches the number of prominent features)
            num_features = len(prominent_features)
            fig, ax = plt.subplots(1, num_features, figsize=(5 * num_features, 5))  # Adjust figsize based on number of features
            # Ensure ax is iterable even if num_features is 1
            ax = ax.flatten() if num_features > 1 else [ax]

            # Plot distribution for each prominent feature
            for i, feature in enumerate(prominent_features):
                sns.histplot(data=cluster_data[cluster], x=feature, bins=20, kde=True, ax=ax[i])
                ax[i].set_title(f'Distribution of {feature} in Cluster {cluster}', fontsize=10)
                ax[i].set_xlabel(feature, fontsize=10)
                ax[i].set_ylabel('Count', fontsize=10)
                ax[i].tick_params(axis='x', rotation=45, labelsize=9)
                # Highlight the peak range (example: print the mode for focus)
                counts, bin_edges = np.histogram(cluster_data[cluster][feature].dropna(), bins=20)
                mode_bin_index = np.argmax(counts)
                mode_value = bin_edges[mode_bin_index] + (bin_edges[1] - bin_edges[0]) / 2  # Midpoint of the peak bin

                # Add vertical line for the mode
                ax[i].axvline(mode_value, color='r', linestyle='--', label=f'Mode (Bin Peak): {mode_value:.2f}')
                ax[i].legend()

            # Adjust layout and display
            plt.tight_layout()
            plt.show()
            
    if underrepresented_clusters:
        print('='*50)
        print("\nDistribution Plots for Prominent Features in Underrepresented Clusters:")
        print(prominent_features, "\n")
        for cluster in underrepresented_clusters:
            # Create figure with subplots (number of subplots matches the number of prominent features)
            num_features = len(prominent_features)
            fig, ax = plt.subplots(1, num_features, figsize=(5 * num_features, 5))  # Adjust figsize based on number of features
            # Ensure ax is iterable even if num_features is 1
            ax = ax.flatten() if num_features > 1 else [ax]

            # Plot distribution for each prominent feature
            for i, feature in enumerate(prominent_features):
                sns.histplot(data=cluster_data[cluster], x=feature, bins=20, kde=True, ax=ax[i])
                ax[i].set_title(f'Distribution of {feature} in Cluster {cluster}', fontsize=14)
                ax[i].set_xlabel(feature, fontsize=10)
                ax[i].set_ylabel('Count', fontsize=10)
                ax[i].tick_params(axis='x', rotation=45, labelsize=9)
                # Highlight the peak range 
                counts, bin_edges = np.histogram(cluster_data[cluster][feature].dropna(), bins=20)
                mode_bin_index = np.argmax(counts)
                mode_value = bin_edges[mode_bin_index] + (bin_edges[1] - bin_edges[0]) / 2  # Midpoint of the peak bin
                # Add vertical line for the mode
                ax[i].axvline(mode_value, color='r', linestyle='--', label=f'Mode (Bin Peak): {mode_value:.2f}')
                ax[i].legend()

            # Adjust layout and display
            plt.tight_layout()
            plt.show()
    return overrepresented_clusters, underrepresented_clusters, cluster_data, centroid_df
In [108]:
overrepresented, underrepresented , clustring_data , centroids_df= difference_plot(preds, pred_customers, customers_pca, customer_sc.columns, pca_35, sc, kmean_model.cluster_centers_)
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Overrepresented clusters (ratio > 1):
Cluster 0: Customer Proportion = 6.73%, General Proportion = 4.44%, Ratio = 1.51
Cluster 1: Customer Proportion = 8.67%, General Proportion = 6.39%, Ratio = 1.36
Cluster 4: Customer Proportion = 0.24%, General Proportion = 0.10%, Ratio = 2.41
Cluster 6: Customer Proportion = 5.36%, General Proportion = 5.13%, Ratio = 1.05
Cluster 9: Customer Proportion = 10.29%, General Proportion = 5.46%, Ratio = 1.88
Cluster 13: Customer Proportion = 12.71%, General Proportion = 4.39%, Ratio = 2.89
Cluster 17: Customer Proportion = 28.50%, General Proportion = 5.74%, Ratio = 4.96
Cluster 18: Customer Proportion = 1.88%, General Proportion = 0.39%, Ratio = 4.86

Underrepresented clusters (ratio < 1):
Cluster 2: Customer Proportion = 4.32%, General Proportion = 4.43%, Ratio = 0.97
Cluster 3: Customer Proportion = 1.18%, General Proportion = 4.70%, Ratio = 0.25
Cluster 5: Customer Proportion = 0.33%, General Proportion = 4.58%, Ratio = 0.07
Cluster 7: Customer Proportion = 1.90%, General Proportion = 5.54%, Ratio = 0.34
Cluster 8: Customer Proportion = 2.45%, General Proportion = 3.93%, Ratio = 0.62
Cluster 10: Customer Proportion = 1.00%, General Proportion = 2.78%, Ratio = 0.36
Cluster 11: Customer Proportion = 0.64%, General Proportion = 7.08%, Ratio = 0.09
Cluster 12: Customer Proportion = 0.50%, General Proportion = 5.45%, Ratio = 0.09
Cluster 14: Customer Proportion = 0.42%, General Proportion = 6.12%, Ratio = 0.07
Cluster 15: Customer Proportion = 4.90%, General Proportion = 6.02%, Ratio = 0.81
Cluster 16: Customer Proportion = 0.22%, General Proportion = 2.84%, Ratio = 0.08
Cluster 19: Customer Proportion = 3.07%, General Proportion = 3.97%, Ratio = 0.77
Cluster 20: Customer Proportion = 2.84%, General Proportion = 6.39%, Ratio = 0.44
Cluster 21: Customer Proportion = 1.85%, General Proportion = 4.11%, Ratio = 0.45

Distribution Plots for Prominent Features in Overrepresented Clusters:
['LP_STATUS_FEIN', 'LP_STATUS_GROB', 'FINANZ_MINIMALIST', 'MOBI_REGIO']
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==================================================

Distribution Plots for Prominent Features in Underrepresented Clusters:
['LP_STATUS_FEIN', 'LP_STATUS_GROB', 'FINANZ_MINIMALIST', 'MOBI_REGIO']
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In [109]:
# What kinds of people are part of a cluster that is overrepresented in the
# customer data compared to the general population?
overrepresented
Out[109]:
[0, 1, 4, 6, 9, 13, 17, 18]
In [110]:
# What kinds of people are part of a cluster that is underrepresented in the
# customer data compared to the general population?
underrepresented
Out[110]:
[2, 3, 5, 7, 8, 10, 11, 12, 14, 15, 16, 19, 20, 21]

Discussion 3.3: Compare Customer Data to Demographics Data¶

  • Overrepresented clusters (ratio > 1):

    • Cluster 0: Customer Proportion = 6.73%, General Proportion = 4.44%, Ratio = 1.51

    • Cluster 1: Customer Proportion = 8.67%, General Proportion = 6.39%, Ratio = 1.36

    • Cluster 4: Customer Proportion = 0.24%, General Proportion = 0.10%, Ratio = 2.41

    • Cluster 6: Customer Proportion = 5.36%, General Proportion = 5.13%, Ratio = 1.05

    • Cluster 9: Customer Proportion = 10.29%, General Proportion = 5.46%, Ratio = 1.88

    • Cluster 13: Customer Proportion = 12.71%, General Proportion = 4.39%, Ratio = 2.89

    • Cluster 17: Customer Proportion = 28.50%, General Proportion = 5.74%, Ratio = 4.96

    • Cluster 18: Customer Proportion = 1.88%, General Proportion = 0.39%, Ratio = 4.86

      • Popular Segments

        • Cluster 0:
          • Proportions: Customer Proportion = 8.29%, General Proportion = 5.09%, Ratio = 1.63
        • Characteristics:
          • Social status: aspiring low-income earners,
          • Social status : low-income earners
          • Financial typology : average
          • Movement patterns : high movement pattern

    So, we can decide the customer segment that relatively popular with the mail-order company. The customer with Social status aspiring low-income earners and low-income earners and Financial typology : average and Movement patterns : high movement pattern

  • Underrepresented clusters (ratio < 1):

    • Cluster 2: Customer Proportion = 4.32%, General Proportion = 4.43%, Ratio = 0.97
    • Cluster 3: Customer Proportion = 1.18%, General Proportion = 4.70%, Ratio = 0.25
    • Cluster 5: Customer Proportion = 0.33%, General Proportion = 4.58%, Ratio = 0.07
    • Cluster 7: Customer Proportion = 1.90%, General Proportion = 5.54%, Ratio = 0.34
    • Cluster 8: Customer Proportion = 2.45%, General Proportion = 3.93%, Ratio = 0.62
    • Cluster 10: Customer Proportion = 1.00%, General Proportion = 2.78%, Ratio = 0.36
    • Cluster 11: Customer Proportion = 0.64%, General Proportion = 7.08%, Ratio = 0.09
    • Cluster 12: Customer Proportion = 0.50%, General Proportion = 5.45%, Ratio = 0.09
    • Cluster 14: Customer Proportion = 0.42%, General Proportion = 6.12%, Ratio = 0.07
    • Cluster 15: Customer Proportion = 4.90%, General Proportion = 6.02%, Ratio = 0.81
    • Cluster 16: Customer Proportion = 0.22%, General Proportion = 2.84%, Ratio = 0.08
    • Cluster 19: Customer Proportion = 3.07%, General Proportion = 3.97%, Ratio = 0.77
    • Cluster 20: Customer Proportion = 2.84%, General Proportion = 6.39%, Ratio = 0.44
    • Cluster 21: Customer Proportion = 1.85%, General Proportion = 4.11%, Ratio = 0.45

      • Popular Segments

        • Cluster 2:
          • Proportions: Customer Proportion = 8.29%, General Proportion = 5.09%, Ratio = 1.63
        • Characteristics:
          • Social status : new houseowners and houseowners So,we can decide the customer segment that relatively unpopular with the mail-order company. A customer who new houseowners and houseowners and low Financial typology and Movement patterns : low movement pattern and very low movement

Congratulations on making it this far in the project! Before you finish, make sure to check through the entire notebook from top to bottom to make sure that your analysis follows a logical flow and all of your findings are documented in Discussion cells. Once you've checked over all of your work, you should export the notebook as an HTML document to submit for evaluation. You can do this from the menu, navigating to File -> Download as -> HTML (.html). You will submit both that document and this notebook for your project submission.

In [ ]: